Exploring ASER2016 Pakistan Data
Yusei Hara
2020/10/1
ASER Pakistan 2016
In this piece of paper, a set of data obtained from Annual Status of Education Report (ASER) is explored. The raw data was downloaded from the link here. https://palnetwork.org/aser-centre/
Preparation
Packages Used
library(tidyverse)
library(ggplot2)
library(ggthemes)
library(ggrepel)
library(gghighlight)
library(stringr)
library(dplyr)
library(sf)
library(scatterplot3d)
library(car)
library(ResourceSelection) #to excute Hosmer-Lemeshow test## Warning: package 'ResourceSelection' was built under R version 4.0.3# library(broom)
require(ggiraph)## Warning: package 'ggiraph' was built under R version 4.0.3require(ggiraphExtra)
# require(plyr)Data Installation
provdist <- read.csv("aser/ASER2016ProvDist.csv")
school <- read.csv("aser/ASER2016GSchool.csv")
child <- read.csv("aser/ASER2016Child.csv")
pschool <- read.csv("aser/ASER2016PvtSchool.csv")
gschool <- read.csv("aser/ASER2016GSchool.csv")
parent <- read.csv("aser/ASER2016Parent.csv")
house <- read.csv("aser/ASER2016HouseholdIndicators.csv")RegionName <- c("2" = "Panjab",
"3" = "Sindh",
"4" = "Balochistan",
"5" = "Khyber Pakhtunkhwa",
"6" = "Gilgit-Baltistan",
"7" = "Azad Jammu and Kashmir",
"8" = "Islamabad - ICT",
"9" = "Federally Administrated Tribal Areas")
Gender <- c("0" = "Male",
"-1" = "Female")Exploration
Checking Samplesizes
length(unique(child$CID))## [1] 255196The whole samplesize (the numebr of children) of this dataset is 255196.
child %>%
filter(DID == 266) %>%
summarize(N_hunza = length(unique(CID)))The samplesize of Hunza alone is 1641.
Exploration in Hunza
Gender Proportion
child %>%
filter(DID == 266) %>%
summarize(gender_proportion = mean(C002))
-1: female, 0: male
gender_proportion = -0.5173675 means there are a little more girls in the dataset.
Age of Children (C001)
child %>%
filter(DID == 266) %>%
ggplot(aes(C001)) +
geom_histogram()
Age is well sparsed
Eduation Status
child %>%
filter(DID == 266) %>%
ggplot(aes(C003)) +
geom_histogram(bins = 3)
1 = never enrolled; 2 = drop-out; 3 = currently enrolled
Education Status by Gender
child %>%
filter(DID == 266) %>%
ggplot(aes(C003)) +
geom_histogram(bins = 3, binwidth = 1) +
facet_grid(~C002, labeller = labeller(C002 = Gender))
Both genders look pretty good interms of the absolute number of currently-enrolled-children
The Enrollment Rate by Gender
child %>%
filter(DID == 266) %>%
group_by(C002) %>%
mutate(enrollment_rate = mean(C003 == 3)) %>%
ggplot(aes(C002, enrollment_rate)) +
geom_col() +
scale_y_continuous() +
geom_label(aes(label = enrollment_rate)) +
scale_x_continuous(breaks = c(-1, 0), labels = c("Female", "Male"))
As a rate, both are doing pretty good
Currently-Enrolled: Institution Type (C006) in Hunza
1 = Government; 2 = Private; 3 = Madrasah(Conventional religious education) School; 4 = Other(Non formal education facility)
child %>%
filter(DID == 266) %>%
ggplot(aes(C006)) +
geom_histogram()
Most children go to public schools or private schools
Within Gilgit-Baltistan
child %>%
filter(RID == 6) %>%
group_by(DID) %>%
mutate(Current_Enrollment_Rate = mean(C003 == 3)) %>%
ggplot(aes(DID, Current_Enrollment_Rate)) +
geom_count() +
scale_x_continuous(breaks = 260:266, labels = c("Gilgit", "Diamer", "Skardu", "Ghanshe", "Astore", "Ghizer", "Hunza-Nagar"))
Within Gilgit-Baltistan, Hunza is outperforming.
Basic Learning Levels: Reading in Local/National Language (C010)
1 = Begginer/Nothing; 2 = Letters; 3 = Words; 4 = Sentences; 5 = Story
child %>%
filter(DID == 266) %>%
ggplot(aes(C010)) +
geom_histogram()
child %>%
filter(DID == 266) %>%
summarize(na = sum(is.na(C010)))Basic Learning Levels by Age
child %>%
filter(DID == 266, C013 != c(3,4)) %>%
ggplot(aes(C010)) +
geom_histogram() +
facet_grid(~C001)
# children at he age of 3 and 4 are removed for they have not dataBasic Learning Levels by Gender
child %>%
filter(DID == 266) %>%
ggplot(aes(C010)) +
geom_histogram() +
facet_grid(~C002, labeller = labeller(C002 = Gender))
English Reading Levels (C013)
child %>%
filter(DID == 266, C013 != c(3,4)) %>%
ggplot(aes(C013)) +
geom_histogram() +
facet_grid(~C001)
# children at he age of 3 and 4 are removed for they have not dataEnglish Reading Levels by Gender
child %>%
filter(DID == 266) %>%
ggplot(aes(C013)) +
geom_histogram() +
facet_grid(~C002, labeller = labeller(C002 = Gender))
Comparison between Other Region
Current Enrollment Rate
child %>%
group_by(DID) %>%
mutate(avg = round(mean(C003 == 3), digits = 2)) %>%
ungroup() %>%
ggplot(aes(avg)) +
geom_histogram() +
facet_grid(~RID, labeller = labeller(RID = RegionName)) +
labs(title = "Current Enrollment Rate by Region")
Female Average Learning Levels (Age or other variables are NOT adjusted)
child %>%
filter(C002 == -1) %>%
group_by(DID) %>%
mutate(avg_learning = mean(C010, na.rm = TRUE)) %>%
ggplot(aes(DID, avg_learning, color = RID)) +
geom_point() +
geom_text(aes(label = DID), nudge_x = 5, check_overlap = TRUE)
child %>%
filter(C002 == -1) %>%
group_by(DID) %>%
mutate(avg_learning = mean(C010, na.rm = TRUE)) %>%
ggplot(aes(DID, avg_learning, color = RID)) +
geom_point() +
geom_text(aes(label = DID), nudge_x = 5, check_overlap = TRUE) +
gghighlight(RID == 6)
It is interesting to note that Gilgit-Baltistan(RID==6) has a huge diversity in average learning levels of girls and Hunza(DID==266) is in the top group of all region.
Spatial Analysis
Districts Map
ica_df <- ica %>%
mutate(centroid = st_centroid(geometry),
x = st_coordinates(centroid)[,1],
y = st_coordinates(centroid)[,2]) %>%
as.data.frame()## Warning: Problem with `mutate()` input `centroid`.
## x st_centroid does not give correct centroids for longitude/latitude data
## i Input `centroid` is `st_centroid(geometry)`.## Warning in st_centroid.sfc(geometry): st_centroid does not give correct
## centroids for longitude/latitude dataica_df <- ica_df %>% select(Province, Districts, x, y)
ica_df <- ica_df %>% summarize(Province = tolower(Province), Districts = tolower(Districts), x = x, y = y)Child and ProvDist data combination
child_dname <- child %>% left_join(provdist[-1])## Joining, by = "DID"child_dname <- child_dname %>% mutate(dname = tolower(DNAME))ica_df_3 <- ica_df %>% filter(Province == "sindh")
ica_df_3$Districts <- ica_df_3$Districts %>%
str_replace("ghotki", "gotki") %>%
str_replace("mirpur khas", "mirpurkhas") %>%
str_replace("malir karachi", "karachi-malir-rural") %>%
str_replace("naushahro feroze", "nowshero feroze") %>%
str_replace("kambar shahdad kot", "qambar shahdadkot") %>%
str_replace("sujawal", "sajawal") %>%
str_replace("shaheed benazir abad", "shaheed benazirabad") %>%
str_replace("tando allahyar", "tando allah yar") %>%
as.vector()
child_dname_3 <- child_dname %>% filter(RNAME == "Sindh") %>% left_join(ica_df_3, by = c("dname" = "Districts"))
child_dname_3 %>% group_by(dname) %>% summarize(n = sum(x))## `summarise()` ungrouping output (override with `.groups` argument)ica_df_3District IDs
ica %>%
mutate(centroid = st_centroid(geometry),
x = st_coordinates(centroid)[,1],
y = st_coordinates(centroid)[,2]) %>%
ggplot() +
geom_sf() +
geom_point(data = child_ica, aes(x, y, label = DID, color = factor(RID))) +
geom_text(data = child_ica, aes(x, y, label = DID), size = 5, nudge_y = 0.2, check_overlap = TRUE)## Warning: Problem with `mutate()` input `centroid`.
## x st_centroid does not give correct centroids for longitude/latitude data
## i Input `centroid` is `st_centroid(geometry)`.## Warning in st_centroid.sfc(geometry): st_centroid does not give correct
## centroids for longitude/latitude data## Warning: Ignoring unknown aesthetics: label## Warning: Removed 8541 rows containing missing values (geom_point).## Warning: Removed 8541 rows containing missing values (geom_text).
Gilgit-Baltistan Region
ica %>%
mutate(centroid = st_centroid(geometry),
x = st_coordinates(centroid)[,1],
y = st_coordinates(centroid)[,2]) %>%
ggplot() +
geom_sf() +
geom_point(data = child_ica, aes(x, y, label = DNAME, shape = factor(RID), color = factor(RID))) +
geom_text(data = child_ica, aes(x, y, label = DNAME), size = 4.5, nudge_y = 0.2, check_overlap = TRUE) +
scale_shape_manual(values = 0:8) +
coord_sf(xlim = c(71, 77.9), ylim = c(34, 37.1))## Warning: Problem with `mutate()` input `centroid`.
## x st_centroid does not give correct centroids for longitude/latitude data
## i Input `centroid` is `st_centroid(geometry)`.## Warning in st_centroid.sfc(geometry): st_centroid does not give correct
## centroids for longitude/latitude data## Warning: Ignoring unknown aesthetics: label## Warning: Removed 8541 rows containing missing values (geom_point).## Warning: Removed 8541 rows containing missing values (geom_text).
Latitudes and longitudes of Gilgit-Baltistan region
child_ica %>%
filter(RID == 6) %>%
summarise(xmin = min(x), xmax = max(x), ymin = min(y), ymax = max(y))Average enrollment rate
ica %>%
mutate(centroid = st_centroid(geometry),
x = st_coordinates(centroid)[,1],
y = st_coordinates(centroid)[,2]) %>%
ggplot() +
geom_sf() +
geom_point(data = child_ica %>% group_by(DID) %>%
mutate(avg_enroll = mean(C003 == 3)),
aes(x, y, label = avg_enroll, color = avg_enroll))
# geom_text(data = child_ica%>% group_by(DID) %>%
# mutate(avg_enroll = mean(C003 == 3)),
# aes(x, y, avg_enroll), check_overlap = TRUE, nudge_y = 0.5)Average enrollment rate of female children
ica %>%
mutate(centroid = st_centroid(geometry),
x = st_coordinates(centroid)[,1],
y = st_coordinates(centroid)[,2]) %>%
ggplot() +
geom_sf() +
geom_point(data = child_ica %>% filter(C002 == -1) %>% group_by(DID) %>%
mutate(avg_enroll = mean(C003 == 3)),
aes(x, y, label = avg_enroll, color = avg_enroll))## Warning: Problem with `mutate()` input `centroid`.
## x st_centroid does not give correct centroids for longitude/latitude data
## i Input `centroid` is `st_centroid(geometry)`.## Warning in st_centroid.sfc(geometry): st_centroid does not give correct
## centroids for longitude/latitude data## Warning: Ignoring unknown aesthetics: label## Warning: Removed 3566 rows containing missing values (geom_point).
ica %>%
mutate(centroid = st_centroid(geometry),
x = st_coordinates(centroid)[,1],
y = st_coordinates(centroid)[,2]) %>%
ggplot() +
geom_sf() +
geom_point(data = child_ica, aes(x, y, label = C010, color = C010))
# geom_text(data = child_ica, aes(x, y, label = C010), check_overlap = TRUE, nudge_y = 1)Traial: children, gender
ica %>%
mutate(centroid = st_centroid(geometry),
x = st_coordinates(centroid)[,1],
y = st_coordinates(centroid)[,2]) %>%
ggplot() +
geom_sf() +
geom_point(data = child_ica %>%
group_by(DID) %>%
mutate(gender_ratio = mean(C002)),
aes(x, y, label = gender_ratio, color = gender_ratio))
0: male, -1: female
Thus, -0.5 indicates the complete gender parity
Children
Average Local/National Language Level Across Ages by Rgion
child_ica %>%
filter(C002 == c(0,-1)) %>%
group_by(DID, C001, C002) %>%
mutate(avg_local = mean(C010, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_local, color = factor(C002), group = DID)) +
geom_line(show.legend = FALSE) +
labs(x = "Age") +
facet_grid(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
facet_wrap(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender))
Average Enrollment Rate
Across Ages by Rgion
child_ica %>%
filter(C002 == c(0,-1)) %>%
group_by(DID, C001, C002) %>%
mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_enrollment, color = factor(DID), group = DID)) +
geom_line(show.legend = FALSE) +
labs(x = "Age") +
facet_grid(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
facet_wrap(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender))
Hunza
child_ica %>%
filter(C002 != "NA", RID == 6, RID != "NA") %>%
group_by(DID, C001, C002) %>%
mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_enrollment, color = factor(DID), group = DID)) +
geom_line() +
labs(x = "Age") +
facet_grid(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
facet_wrap(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
gghighlight(label_key = DNAME, calculate_per_facet = TRUE)
child_ica %>%
filter(C002 != "NA", RID == 6, RID != "NA") %>%
group_by(DID, C001, C002) %>%
mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_enrollment, color = factor(DID), group = DID)) +
geom_line() +
labs(x = "Age") +
facet_grid(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
facet_wrap(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
gghighlight(DID == 266,label_key = DNAME, calculate_per_facet = TRUE)## Warning: Tried to calculate with group_by(), but the calculation failed.
## Falling back to ungrouped filter operation...
Karachi
child_ica %>%
filter(C002 != "NA", RID == 3) %>%
group_by(DID, C001, C002) %>%
mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_enrollment, color = factor(DID), group = DID)) +
geom_line() +
labs(x = "Age") +
facet_grid(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
facet_wrap(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
gghighlight(DID == c(315,316), label_key = DNAME, calculate_per_facet = TRUE)
children Education Stage
child_ica %>%
filter(PR004 != "NA", PR009 != "NA", C010 != "NA", C002 != "NA") %>%
group_by(C005) %>%
summarize(n = n()) %>%
mutate(C005 = fct_reorder(C005, n)) %>%
ggplot(aes(C005, n)) +
geom_col() +
coord_flip()## `summarise()` ungrouping output (override with `.groups` argument)
Hunza. Children Education Stage
child_ica %>%
filter(PR004 != "NA", PR009 != "NA", !is.na(C010), C002 != "NA", !is.na(C005), DID == 266) %>%
group_by(C005) %>%
summarize(n = n()) %>%
mutate(C005 = fct_reorder(C005, n)) %>%
ggplot(aes(C005, n)) +
geom_col() +
coord_flip()## `summarise()` ungrouping output (override with `.groups` argument)
Enrollment and Gender within Gilgit-Baltistan
child_ica %>%
filter(!is.na(C002), RID == 6) %>%
group_by(DID, C001, C002) %>%
mutate(enroll = mean(C003 == 3)) %>%
ggplot(aes(C001, enroll, group = C002, color = factor(C002))) +
geom_line() +
facet_wrap(.~DID)
Education Etages
child_ica %>%
group_by(DID) %>%
mutate(n = length(C003),
dropout = sum(C003 == 2),
dropout_ratio = mean(C003 == 2),
never = sum(C003 == 1),
never_ratio = mean(C003 == 1)) %>%
ggplot(aes(DID, never_ratio, color = factor(RID))) +
geom_point()
Child & Parents
Mother Enrollment by District
child_ica %>%
filter(RID == 6, PR004 != "NA") %>%
group_by(DID, PR004) %>%
mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>%
ggplot(aes(DID, avg_enrollment, fill = factor(PR004))) +
geom_col(aes(factor(DID), avg_enrollment, fill = factor(PR004)), position = "dodge")
Within Gilgit-Baltistan, Hunza is the only district where there are more women who have experience of education than those who have never enrolled in schools.
Father Enrollment by District
child_ica %>%
filter(RID == 6, PR009 != "NA") %>%
group_by(DID, PR009) %>%
mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>%
ggplot(aes(DID, avg_enrollment, group = PR009, fill = factor(PR009))) +
geom_col(aes(factor(DID), avg_enrollment), position = "dodge")
Mother’s Education and Children Learning Level by Region. boxplot
child_ica %>%
filter(PR009 != "NA", PR004 != "NA") %>%
group_by(DID) %>%
mutate(avg_learning = mean(C003 == 3)) %>%
ggplot(aes(PR004, avg_learning, group = PR004)) +
geom_boxplot(aes(PR004, avg_learning)) +
facet_grid(.~RID)
Father’s Education and Children Learning Level by Region. boxplot
child_ica %>%
filter(PR009 != "NA", PR004 != "NA") %>%
group_by(DID) %>%
mutate(avg_learning = mean(C003 == 3)) %>%
ggplot(aes(PR009, avg_learning, group = PR009)) +
geom_boxplot(aes(PR009, avg_learning)) +
facet_grid(.~RID)
Mother’s Education and Children Learning Level by District
child_ica %>%
filter(PR009 != "NA", PR004 != "NA") %>%
group_by(DID) %>%
mutate(avg_learning = mean(C003 == 3, na.rm = TRUE), mother_enrollment = mean(PR004, na.rm = TRUE)) %>%
ggplot(aes(mother_enrollment, avg_learning)) +
geom_point()
child_ica %>%
filter(PR009 != "NA", PR004 != "NA") %>%
group_by(DID) %>%
mutate(avg_learning = mean(C003 == 3, na.rm = TRUE), mother_enrollment = mean(PR004, na.rm = TRUE)) %>%
ungroup() %>%
summarize(r = cor(mother_enrollment, avg_learning))Father’s Education and Children Learning Level by District
child_ica %>%
filter(PR009 != "NA", PR004 != "NA") %>%
group_by(DID) %>%
mutate(avg_learning = mean(C003 == 3, na.rm = TRUE), father_enrollment = mean(PR009, na.rm = TRUE)) %>%
ggplot(aes(father_enrollment, avg_learning)) +
geom_point()
child_ica %>%
filter(PR009 != "NA", PR004 != "NA") %>%
group_by(DID) %>%
mutate(avg_learning = mean(C003 == 3), father_enrollment = mean(PR009)) %>%
ungroup() %>%
summarize(r = cor(father_enrollment, avg_learning))Hunza. Age, Learning Level, Mother’s Education,
child_ica %>%
filter(PR009 != "NA", PR004 != "NA", C003 != "NA", DID == 266, C001 != "NA") %>%
group_by(PR004, C001) %>%
mutate(avg_learning = mean(C010, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_learning, group = PR004, color = factor(PR004))) +
geom_line(aes(C001, avg_learning))## Warning: Removed 200 row(s) containing missing values (geom_path).
Hunza. Age, Learning Level, Father’s Education
child_ica %>%
filter(PR009 != "NA", PR004 != "NA", C003 != "NA", DID == 266, C001 != "NA") %>%
group_by(PR009, C001) %>%
mutate(avg_learning = mean(C010, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_learning, group = PR009, color = factor(PR009))) +
geom_line(aes(C001, avg_learning))## Warning: Removed 200 row(s) containing missing values (geom_path).
Gilgit-Baltistan. Age, Learning Level, Mother’s Education
child_ica %>%
filter(PR009 != "NA",
PR004 != "NA",
C003 != "NA",
C001 != "NA",
RID == 6) %>%
group_by(DID, PR004, C001) %>%
mutate(avg_learning = mean(C010, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_learning, group = PR004, color = factor(PR004))) +
geom_line(aes(C001, avg_learning)) +
facet_grid(.~DID) +
facet_wrap(.~DID)## Warning: Removed 226 row(s) containing missing values (geom_path).
Gilgit-Baltistan. Age, Learning Level, Father’s Education
child_ica %>%
filter(PR009 != "NA",
PR004 != "NA",
C003 != "NA",
C001 != "NA",
RID == 6) %>%
group_by(DID, PR009, C001) %>%
mutate(avg_learning = mean(C010, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_learning, group = PR009, color = factor(PR009))) +
geom_line(aes(C001, avg_learning)) +
facet_grid(.~DID) +
facet_wrap(.~DID)## Warning: Removed 226 row(s) containing missing values (geom_path).
Gilgit-Baltistan. Age, Enrollment, Mother’s Education
child_ica %>%
filter(PR009 != "NA",
PR004 != "NA",
C003 != "NA",
C001 != "NA",
RID == 6) %>%
group_by(DID, PR004, C001) %>%
mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_enrollment, group = PR004, color = factor(PR004))) +
geom_line(aes(C001, avg_enrollment)) +
facet_grid(.~DID) +
facet_wrap(.~DID)
Gilgit-Baltistan. age, enrollment, father’s education
child_ica %>%
filter(PR009 != "NA",
PR004 != "NA",
C003 != "NA",
C001 != "NA",
RID == 6) %>%
group_by(DID, PR009, C001) %>%
mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_enrollment, group = PR009, color = factor(PR009))) +
geom_line(aes(C001, avg_enrollment)) +
facet_grid(.~DID) +
facet_wrap(.~DID)
mothers education level
child_ica %>%
filter(PR004 != "NA", PR009 != "NA", C010 != "NA", C002 != "NA") %>%
ggplot(aes(PR005)) +
geom_histogram(stat = "count") +
coord_flip()## Warning: Ignoring unknown parameters: binwidth, bins, pad
summary
child_ica %>%
ggplot(aes(PR006)) +
geom_histogram(stat = "count") +
coord_flip()## Warning: Ignoring unknown parameters: binwidth, bins, pad
fathers education level
child_ica %>%
filter(PR004 != "NA", PR009 != "NA", C010 != "NA", C002 != "NA") %>%
ggplot(aes(PR010)) +
geom_histogram(stat = "count") +
coord_flip()## Warning: Ignoring unknown parameters: binwidth, bins, pad
###### summary
child_ica %>%
ggplot(aes(PR011)) +
geom_histogram(stat = "count") +
coord_flip()## Warning: Ignoring unknown parameters: binwidth, bins, pad
mothers education level by region
child_ica %>%
group_by(DID, PR006) %>%
ggplot(aes(PR006)) +
geom_histogram(aes(PR006), stat = "count") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 5)) +
facet_grid(.~RID)## Warning: Ignoring unknown parameters: binwidth, bins, pad
Mother enrollment by district
child_ica %>%
filter(PR004 != "NA", PR009 != "NA", C010 != "NA") %>%
group_by(DID) %>%
mutate(mother_edu_rate = sum(PR004 == -1)/length(PR004)) %>%
ggplot(aes(DID, mother_edu_rate, shape = factor(RID), color = factor(RID))) +
geom_point() +
scale_shape_manual(values = 0:8)
Gilgit-Baltistan. Average mother education experience, House type
child_ica %>%
filter(PR004 != "NA", PR009 != "NA", C010 != "NA") %>%
group_by(DID) %>%
mutate(mother_edu_rate = sum(PR004 == -1)/length(PR004)) %>% ungroup() %>%
ggplot(aes(factor(RID), mother_edu_rate, group = RID)) +
geom_violin()
Hunza. father enroll ratio
child_ica %>%
filter(DID == 266, !is.na(PR009)) %>%
summarize(father_edu_ratio = sum(PR009 == -1)/length(PR009))father enroll ratio
child_ica %>%
filter(!is.na(PR009)) %>%
summarize(father_edu_ratio = sum(PR009 == -1)/length(PR009))father enroll ratio by district
child_ica %>%
filter(!is.na(PR009)) %>%
group_by(DID) %>%
mutate(father_edu_ratio = sum(PR009 == -1)/length(PR009)) %>%
ggplot(aes(factor(DID), father_edu_ratio)) +
geom_point()
mother enroll ratio by district
child_ica %>%
filter(!is.na(PR009), !is.na(PR004)) %>%
group_by(DID) %>%
mutate(mother_edu_ratio = sum(PR004 == -1)/length(PR004)) %>%
ggplot(aes(factor(DID), mother_edu_ratio)) +
geom_point()
Child, Household
Number of Children in a household
child_ica %>%
filter(!is.na(PR004), !is.na(PR009)) %>%
filter(HHID == 96546) %>%
select(CID, PRID, C002, C001, PR001, VID, C003, DID, RID)child_ica %>%
group_by(HHID) %>%
mutate(n = n()) %>%
ggplot(aes(factor(n), fill = factor(RID))) +
geom_bar(position = "dodge") +
facet_grid(.~RID) +
facet_wrap(.~RID)
house type ratio by region
child_ica %>%
ggplot(aes(x = factor(DID), y = factor(H002), fill = factor(H002))) +
geom_bar(aes(x = factor(DID), y = factor(H002)), stat = "identity", position = "fill") +
facet_grid(.~RID, scales = "free") +
facet_wrap(.~RID, scales = "free") +
theme(axis.text.x = element_text(angle = 90, size = 7, hjust = 1)) +
coord_cartesian(ylim = c(0,1), clip = "off", expand = FALSE)
Gilgit-Baltistan. house type
child_ica %>%
filter(C003 != "NA", RID == 6) %>%
group_by(DID, H002) %>%
mutate(avg_enrollment = mean(C003 == 3)) %>%
ggplot(aes(factor(DID), avg_enrollment, group = H002, fill = factor(H002))) +
geom_col(position = "dodge")
Hunza. house type
child_ica %>% left_join(house, by = "HHID") %>%
filter(DID.x == 266) %>%
ggplot(aes(H002.x)) +
geom_histogram(aes(H002.x), stat = "count")## Warning: Ignoring unknown parameters: binwidth, bins, pad
Average Number of Children in a household
child_ica %>%
group_by(HHID) %>%
mutate(n_children = length(unique(CID))) %>%
ungroup() %>%
group_by(DID) %>%
mutate(avg_n_children = mean(n_children)) %>%
ggplot(aes(DID, avg_n_children, color = factor(RID))) +
geom_point()
child_ica %>%
ggplot(aes(C005)) +
geom_histogram(stat = "count") +
coord_flip()## Warning: Ignoring unknown parameters: binwidth, bins, pad
Hunza, house type
child_ica %>%
filter(DID == 266) %>%
group_by(H002) %>%
summarize(n = n()) %>%
ggplot(aes(H002, n)) +
geom_col() +
geom_text(aes(label = n), vjust = 1.5, color = "white") +
xlab("House Type") +
ylab("Number of Households")## `summarise()` ungrouping output (override with `.groups` argument)
Hunza, number of children in households
child_ica%>%
filter(DID == 266) %>%
group_by(HHID) %>%
summarize(n = length(unique(CID))) %>%
ungroup() %>%
group_by(n) %>%
summarize(num = n()) %>%
ggplot(aes(factor(n), num)) +
geom_col() +
geom_text(aes(label = num), vjust = -0.5) +
xlab("Number of Children in Each Household") +
ylab("Number of Households") ## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
Paid-Tuition
child_ica %>%
filter(PR009 == 0, !is.na(C008a)) %>%
summarize(n = length(unique(CID)),
paid_tuition = sum(C008a == -1),
ratio = paid_tuition/n)Hunza
child_ica %>%
filter(DID == 266, PR009 == 0, !is.na(C008a)) %>%
summarize(n = length(unique(CID)),
paid_tuition = sum(C008a == -1),
ratio = paid_tuition/n)child_ica %>%
filter(DID == 266, PR004 == 0, !is.na(C008a)) %>%
summarize(n = length(unique(CID)),
paid_tuition = sum(C008a == -1),
ratio = paid_tuition/n)paid tuition by district
child_ica %>%
group_by(DID) %>%
mutate(avg_paid = mean(C008a == -1, na.rm = TRUE)) %>%
ggplot(aes(DID, avg_paid)) +
geom_point()+
scale_y_continuous(limits = c(0,1))
child_ica %>%
filter(!is.na(C002)) %>%
group_by(DID) %>%
summarize(avg_paid = mean(C008a == -1),
female = mean(child_ica %>% filter(C002 == -1) %>%
group_by(DID) %>% .$C003 == 3),
male = mean(child_ica %>% filter(C002 == 0) %>%
group_by(DID) %>% .$C003 == 3),
ratio = female/male)## `summarise()` ungrouping output (override with `.groups` argument)child_ica %>%
filter(!is.na(C002)) %>%
group_by(DID, C002) %>%
summarize(enroll = mean(C003 == 3)) %>%
ungroup() %>%
spread(C002, enroll) %>%
summarize(DID = DID,
gender_ratio = .$"-1"/.$"0") %>%
ggplot(aes(DID, gender_ratio)) +
geom_point() +
scale_y_continuous(limits = c(0,1))## `summarise()` regrouping output by 'DID' (override with `.groups` argument)## Warning: Removed 16 rows containing missing values (geom_point).
correlation b/w paid-tuition and enrollment?
child_ica %>%
filter(!is.na(C002)) %>%
group_by(DID) %>%
mutate(paid_tuition = mean(C008a == -1, na.rm = TRUE)) %>%
group_by(DID, C002) %>%
mutate(enroll = mean(C003 == 3)) %>%
ggplot(aes(paid_tuition, enroll)) +
geom_point() +
geom_smooth() +
facet_grid(.~C002)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
VID
Gilgit (DID == 260)
child_ica %>%
filter(!is.na(C002), DID == 260) %>%
group_by(VID, C002) %>%
summarize(avg_enroll = mean(C003 == 3)) %>%
ggplot(aes(VID, avg_enroll, color = factor(C002))) +
geom_point()## `summarise()` regrouping output by 'VID' (override with `.groups` argument)
Preparation for Logistic Regression Analysis
Making Dataframe With Dummy Variables
child_ica_dummy <- child_ica %>% filter(!is.na(C002), !is.na(C003), !is.na(PR004), !is.na(PR009))
child_ica_dummy$C002_01 <- ifelse(child_ica_dummy$C002 == -1, 1, 0)
child_ica_dummy$C003_01 <- ifelse(child_ica_dummy$C003 == 3, 1, 0)
child_ica_dummy$PR004_01 <- ifelse(child_ica_dummy$PR004 == -1, 1, 0)
child_ica_dummy$PR009_01 <- ifelse(child_ica_dummy$PR009 == -1, 1, 0)
child_ica_dummy$PR004_PR009_01 <- ifelse(child_ica_dummy$PR004 == -1 | child_ica_dummy$PR009 == -1, 1, 0)n_children_in_household <- child_ica_dummy %>%
group_by(HHID) %>%
summarize(n_children_in_household = length(unique(CID)))## `summarise()` ungrouping output (override with `.groups` argument)child_ica_dummy <- child_ica_dummy %>% left_join(n_children_in_household)## Joining, by = "HHID"child_ica_dummy$H002_1_01 <- ifelse(child_ica_dummy$H002 == 1, 1, 0)
child_ica_dummy$H002_2_01 <- ifelse(child_ica_dummy$H002 == 2, 1, 0)
child_ica_dummy$H002_3_01 <- ifelse(child_ica_dummy$H002 == 3, 1, 0)Create dummy variable with regard to region
child_ica_dummy <- child_ica_dummy
child_ica_dummy$Panjab <- ifelse(child_ica_dummy$RID == 2, 1, 0)
child_ica_dummy$Sindh <- ifelse(child_ica_dummy$RID == 3, 1, 0)
child_ica_dummy$Balochistan <- ifelse(child_ica_dummy$RID == 4, 1, 0)
child_ica_dummy$Khyber_Pakhtunkhwa <- ifelse(child_ica_dummy$RID == 5, 1, 0)
child_ica_dummy$Gilgit_Baltistan <- ifelse(child_ica_dummy$RID == 6, 1, 0)
child_ica_dummy$Azad_Jammu_and_Kashmir <- ifelse(child_ica_dummy$RID == 7, 1, 0)
child_ica_dummy$Islamabad_ICT <- ifelse(child_ica_dummy$RID == 8, 1, 0)
child_ica_dummy$Federally_Administrated_Tribal_Areas <- ifelse(child_ica_dummy$RID == 9, 1, 0)Eliminated NAs
child_ica %>%
summarize(C002_na = sum(is.na(C002)),
C003_na = sum(is.na(C003)),
PR004_na = sum(is.na(PR004)),
PR009_na = sum(is.na(PR009)))Eliminated Rows in Total
data.frame(original_rows = nrow(child_ica),
eliminated_rows = nrow(child_ica) - nrow(child_ica_dummy),
ratio = (nrow(child_ica)-nrow(child_ica_dummy))/nrow(child_ica))Eliminated NAs Hunza
child_ica %>%
filter(DID == 266) %>%
summarize(C002_na = sum(is.na(C002)),
C003_na = sum(is.na(C003)),
PR004_na = sum(is.na(PR004)),
PR009_na = sum(is.na(PR009)))Eliminated Rows in Total Hunza
data.frame(original_rows = nrow(child_ica %>% filter(DID == 266)),
eliminated_rows = nrow(child_ica %>% filter(DID == 266)) - nrow(child_ica_dummy %>% filter(DID == 266)),
ratio = (nrow(child_ica %>% filter(DID == 266) %>% filter(DID == 266))-nrow(child_ica_dummy %>% filter(DID == 266)))/nrow(child_ica %>% filter(DID == 266)))Generalized Linear Models
GLM C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01
glm_child <- glm(C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, family = "binomial", data = child_ica_dummy)
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01,
## family = "binomial", data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8850 -1.2383 0.6563 0.8621 1.3071
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.934846 0.013780 67.84 <2e-16 ***
## n_children_in_household -0.055206 0.002935 -18.81 <2e-16 ***
## C002_01 -0.572083 0.009041 -63.28 <2e-16 ***
## PR004_PR009_01 0.711645 0.009062 78.53 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 289072 on 245743 degrees of freedom
## AIC: 289080
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 490.13, df = 8, p-value < 2.2e-16Hunza. C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01
glm_child_hunza <- glm(C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
ggPredict(glm_child_hunza, se = TRUE, show.summary = TRUE, point = TRUE, colorAsFactor = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01,
## family = "binomial", data = child_ica_dummy %>% filter(DID ==
## 266))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5255 0.3335 0.3611 0.4126 0.6056
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.74787 0.35406 7.761 8.43e-15 ***
## n_children_in_household -0.16354 0.07071 -2.313 0.0207 *
## C002_01 0.12227 0.19347 0.632 0.5274
## PR004_PR009_01 0.44052 0.21451 2.054 0.0400 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 838.79 on 1609 degrees of freedom
## Residual deviance: 826.06 on 1606 degrees of freedom
## AIC: 834.06
##
## Number of Fisher Scoring iterations: 5## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child_hunza$y, y = fitted(glm_child_hunza))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child_hunza$y, fitted(glm_child_hunza)
## X-squared = 9.9762, df = 8, p-value = 0.2667exponential transformation
exp(glm_child_hunza$coefficients)## (Intercept) n_children_in_household C002_01
## 15.6093953 0.8491308 1.1300567
## PR004_PR009_01
## 1.5535098confidence interval (intercept and coefficient)
confint(glm_child_hunza, level = 0.95)## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 2.0690843 3.45821705
## n_children_in_household -0.3018419 -0.02433219
## C002_01 -0.2575389 0.50262655
## PR004_PR009_01 0.0115443 0.85445608exponential transformation of confidence interval (odds ratio of intercept and coefficient)
exp(confint(glm_child_hunza, level = 0.95))## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 7.9175694 31.7602989
## n_children_in_household 0.7394550 0.9759614
## C002_01 0.7729515 1.6530574
## PR004_PR009_01 1.0116112 2.3500958AIC
extractAIC(glm_child_hunza)## [1] 4.0000 834.0574BIC
extractAIC(glm_child_hunza, k = log(nrow(glm_child_hunza$data)))## [1] 4.0000 855.5934effectiveness of explanatory variables
glm_child_hunza_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
anova <- anova(glm_child_hunza_null, glm_child_hunza, test = "Chisq")
anovavariables selection
step(glm_child_hunza_null, direction = "both",
scope = (~ C001 + C002_01 + PR004_01 + PR009_01))## Start: AIC=840.79
## C003_01 ~ 1
##
## Df Deviance AIC
## + C001 1 732.49 736.49
## + PR004_01 1 824.25 828.25
## + PR009_01 1 830.82 834.82
## <none> 838.79 840.79
## + C002_01 1 838.58 842.58
##
## Step: AIC=736.49
## C003_01 ~ C001
##
## Df Deviance AIC
## + PR004_01 1 707.11 713.11
## + PR009_01 1 717.26 723.26
## <none> 732.49 736.49
## + C002_01 1 732.49 738.49
## - C001 1 838.79 840.79
##
## Step: AIC=713.11
## C003_01 ~ C001 + PR004_01
##
## Df Deviance AIC
## <none> 707.11 713.11
## + PR009_01 1 705.74 713.74
## + C002_01 1 707.11 715.11
## - PR004_01 1 732.49 736.49
## - C001 1 824.25 828.25##
## Call: glm(formula = C003_01 ~ C001 + PR004_01, family = "binomial",
## data = child_ica_dummy %>% filter(DID == 266))
##
## Coefficients:
## (Intercept) C001 PR004_01
## -0.4789 0.3039 1.0316
##
## Degrees of Freedom: 1609 Total (i.e. Null); 1607 Residual
## Null Deviance: 838.8
## Residual Deviance: 707.1 AIC: 713.1multicolinearity
vif(glm_child_hunza)## n_children_in_household C002_01 PR004_PR009_01
## 1.087196 1.006453 1.080729GLM C003_01 ~ n_children_in_household + H002 + PR004_PR009_01
glm_child <- glm(C003_01 ~ n_children_in_household + H002 + PR004_PR009_01, family = "binomial", data = child_ica_dummy)
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + H002 + PR004_PR009_01,
## family = "binomial", data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.927 -1.297 0.723 0.869 1.186
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.094686 0.016614 5.699 1.2e-08 ***
## n_children_in_household -0.049487 0.002933 -16.871 < 2e-16 ***
## H002 0.379659 0.006366 59.636 < 2e-16 ***
## PR004_PR009_01 0.502871 0.009477 53.061 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 289446 on 245743 degrees of freedom
## AIC: 289454
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 355.01, df = 8, p-value < 2.2e-16Hunza. C003_01 ~ n_children_in_household + H002 + PR004_PR009_01
glm_child_hunza <- glm(C003_01 ~ n_children_in_household + H002 + PR004_PR009_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
ggPredict(glm_child_hunza, se = TRUE, show.summary = TRUE, point = TRUE, colorAsFactor = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + H002 + PR004_PR009_01,
## family = "binomial", data = child_ica_dummy %>% filter(DID ==
## 266))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6103 0.3376 0.3660 0.4038 0.5903
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.23266 0.49678 6.507 7.65e-11 ***
## n_children_in_household -0.16663 0.07002 -2.380 0.0173 *
## H002 -0.20362 0.16445 -1.238 0.2157
## PR004_PR009_01 0.51067 0.22107 2.310 0.0209 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 838.79 on 1609 degrees of freedom
## Residual deviance: 824.91 on 1606 degrees of freedom
## AIC: 832.91
##
## Number of Fisher Scoring iterations: 5## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child_hunza$y, y = fitted(glm_child_hunza))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child_hunza$y, fitted(glm_child_hunza)
## X-squared = 15.469, df = 8, p-value = 0.05064exponential transformation
exp(glm_child_hunza$coefficients)## (Intercept) n_children_in_household H002
## 25.3469307 0.8465115 0.8157746
## PR004_PR009_01
## 1.6664083confidence interval (intercept and coefficient)
confint(glm_child_hunza, level = 0.95)## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 2.27327932 4.22193092
## n_children_in_household -0.30339827 -0.02854365
## H002 -0.52934555 0.11610748
## PR004_PR009_01 0.06943756 0.93802587exponential transformation of confidence interval (odds ratio of intercept and coefficient)
exp(confint(glm_child_hunza, level = 0.95))## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 9.7111948 68.1649785
## n_children_in_household 0.7383050 0.9718599
## H002 0.5889903 1.1231166
## PR004_PR009_01 1.0719051 2.5549327AIC
extractAIC(glm_child_hunza)## [1] 4.0000 832.9071BIC
extractAIC(glm_child_hunza, k = log(nrow(glm_child_hunza$data)))## [1] 4.0000 854.4431effectiveness of explanatory variables
glm_child_hunza_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
anova <- anova(glm_child_hunza_null, glm_child_hunza, test = "Chisq")
anovavariables selection
step(glm_child_hunza_null, direction = "both",
scope = (~ C001 + C002_01 + PR004_01 + PR009_01))## Start: AIC=840.79
## C003_01 ~ 1
##
## Df Deviance AIC
## + C001 1 732.49 736.49
## + PR004_01 1 824.25 828.25
## + PR009_01 1 830.82 834.82
## <none> 838.79 840.79
## + C002_01 1 838.58 842.58
##
## Step: AIC=736.49
## C003_01 ~ C001
##
## Df Deviance AIC
## + PR004_01 1 707.11 713.11
## + PR009_01 1 717.26 723.26
## <none> 732.49 736.49
## + C002_01 1 732.49 738.49
## - C001 1 838.79 840.79
##
## Step: AIC=713.11
## C003_01 ~ C001 + PR004_01
##
## Df Deviance AIC
## <none> 707.11 713.11
## + PR009_01 1 705.74 713.74
## + C002_01 1 707.11 715.11
## - PR004_01 1 732.49 736.49
## - C001 1 824.25 828.25##
## Call: glm(formula = C003_01 ~ C001 + PR004_01, family = "binomial",
## data = child_ica_dummy %>% filter(DID == 266))
##
## Coefficients:
## (Intercept) C001 PR004_01
## -0.4789 0.3039 1.0316
##
## Degrees of Freedom: 1609 Total (i.e. Null); 1607 Residual
## Null Deviance: 838.8
## Residual Deviance: 707.1 AIC: 713.1# step(glm_child_null, direction = "both",
# scope = (~ C001 + C002 + PR004_01 + PR009_01 +
# Panjab + Sindh + Balochistan + Khyber_Pakhtunkhwa + Gilgit_Baltistan +
# Azad_Jammu_and_Kashmir + Islamabad_ICT))multicolinearity
vif(glm_child_hunza)## n_children_in_household H002 PR004_PR009_01
## 1.083416 1.088995 1.147110GLM C003_01 ~ n_children_in_household + PR004_PR009_01 + H002
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + H002, family = "binomial", data = child_ica_dummy)
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## H002, family = "binomial", data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.927 -1.297 0.723 0.869 1.186
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.094686 0.016614 5.699 1.2e-08 ***
## n_children_in_household -0.049487 0.002933 -16.871 < 2e-16 ***
## PR004_PR009_01 0.502871 0.009477 53.061 < 2e-16 ***
## H002 0.379659 0.006366 59.636 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 289446 on 245743 degrees of freedom
## AIC: 289454
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 355.01, df = 8, p-value < 2.2e-16Hunza. C003_01 ~ n_children_in_household + PR004_PR009_01 + H002
glm_child_hunza <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + H002, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
ggPredict(glm_child_hunza, se = TRUE, show.summary = TRUE, point = TRUE, colorAsFactor = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## H002, family = "binomial", data = child_ica_dummy %>% filter(DID ==
## 266))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6103 0.3376 0.3660 0.4038 0.5903
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.23266 0.49678 6.507 7.65e-11 ***
## n_children_in_household -0.16663 0.07002 -2.380 0.0173 *
## PR004_PR009_01 0.51067 0.22107 2.310 0.0209 *
## H002 -0.20362 0.16445 -1.238 0.2157
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 838.79 on 1609 degrees of freedom
## Residual deviance: 824.91 on 1606 degrees of freedom
## AIC: 832.91
##
## Number of Fisher Scoring iterations: 5## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 355.01, df = 8, p-value < 2.2e-16Multicolinearity house type and parent edu?
child_ica_dummy %>%
group_by(H002) %>%
mutate(n_parents = length(PR004_PR009_01))%>%
ggplot(aes(H002, n_parents, fill = factor(PR004_PR009_01))) +
geom_col(position = "stack") +
xlab("Type of Houses") +
ylab("Number of Parents") +
labs(fill = "Parents Education\n(at least one of them, primary or more)")
The more educated the parents are, the better house they are living in.
GLM C003_01 ~ n_children_in_household + H002 + C002_01
glm_child <- glm(C003_01 ~ n_children_in_household + H002 + C002_01, family = "binomial", data = child_ica_dummy)
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + H002 + C002_01,
## family = "binomial", data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0122 -1.2391 0.7184 0.8909 1.2724
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.424111 0.016894 25.10 <2e-16 ***
## n_children_in_household -0.052085 0.002941 -17.71 <2e-16 ***
## H002 0.503563 0.006129 82.16 <2e-16 ***
## C002_01 -0.575354 0.009055 -63.54 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 288199 on 245743 degrees of freedom
## AIC: 288207
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 818.15, df = 8, p-value < 2.2e-16Hunza. C003_01 ~ n_children_in_household + H002 + C002_01
glm_child_hunza <- glm(C003_01 ~ n_children_in_household + H002 + C002_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
ggPredict(glm_child_hunza, se = TRUE, show.summary = TRUE, point = TRUE, colorAsFactor = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + H002 + C002_01,
## family = "binomial", data = child_ica_dummy %>% filter(DID ==
## 266))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5804 0.3373 0.3731 0.4124 0.5843
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.48657 0.48393 7.205 5.82e-13 ***
## n_children_in_household -0.20822 0.06887 -3.023 0.0025 **
## H002 -0.11589 0.15847 -0.731 0.4646
## C002_01 0.13022 0.19320 0.674 0.5003
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 838.79 on 1609 degrees of freedom
## Residual deviance: 829.57 on 1606 degrees of freedom
## AIC: 837.57
##
## Number of Fisher Scoring iterations: 5## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child_hunza$y, y = fitted(glm_child_hunza))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child_hunza$y, fitted(glm_child_hunza)
## X-squared = 12.015, df = 8, p-value = 0.1505exponential transformation
exp(glm_child_hunza$coefficients)## (Intercept) n_children_in_household H002
## 32.6735833 0.8120302 0.8905720
## C002_01
## 1.1390753confidence interval (intercept and coefficient)
confint(glm_child_hunza, level = 0.95)## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 2.5525708 4.45124970
## n_children_in_household -0.3424699 -0.07212852
## H002 -0.4291928 0.19275626
## C002_01 -0.2490334 0.51006070exponential transformation of confidence interval (odds ratio of intercept and coefficient)
exp(confint(glm_child_hunza, level = 0.95))## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 12.8400705 85.7340185
## n_children_in_household 0.7100145 0.9304113
## H002 0.6510344 1.2125872
## C002_01 0.7795540 1.6653923AIC
extractAIC(glm_child_hunza)## [1] 4.0000 837.5663BIC
extractAIC(glm_child_hunza, k = log(nrow(glm_child_hunza$data)))## [1] 4.0000 859.1022effectiveness of explanatory variables
glm_child_hunza_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
anova <- anova(glm_child_hunza_null, glm_child_hunza, test = "Chisq")
anovavariables selection
step(glm_child_hunza_null, direction = "both",
scope = (~ C001 + C002_01 + PR004_01 + PR009_01))## Start: AIC=840.79
## C003_01 ~ 1
##
## Df Deviance AIC
## + C001 1 732.49 736.49
## + PR004_01 1 824.25 828.25
## + PR009_01 1 830.82 834.82
## <none> 838.79 840.79
## + C002_01 1 838.58 842.58
##
## Step: AIC=736.49
## C003_01 ~ C001
##
## Df Deviance AIC
## + PR004_01 1 707.11 713.11
## + PR009_01 1 717.26 723.26
## <none> 732.49 736.49
## + C002_01 1 732.49 738.49
## - C001 1 838.79 840.79
##
## Step: AIC=713.11
## C003_01 ~ C001 + PR004_01
##
## Df Deviance AIC
## <none> 707.11 713.11
## + PR009_01 1 705.74 713.74
## + C002_01 1 707.11 715.11
## - PR004_01 1 732.49 736.49
## - C001 1 824.25 828.25##
## Call: glm(formula = C003_01 ~ C001 + PR004_01, family = "binomial",
## data = child_ica_dummy %>% filter(DID == 266))
##
## Coefficients:
## (Intercept) C001 PR004_01
## -0.4789 0.3039 1.0316
##
## Degrees of Freedom: 1609 Total (i.e. Null); 1607 Residual
## Null Deviance: 838.8
## Residual Deviance: 707.1 AIC: 713.1# step(glm_child_null, direction = "both",
# scope = (~ C001 + C002 + PR004_01 + PR009_01 +
# Panjab + Sindh + Balochistan + Khyber_Pakhtunkhwa + Gilgit_Baltistan +
# Azad_Jammu_and_Kashmir + Islamabad_ICT))multicolinearity
vif(glm_child_hunza)## n_children_in_household H002 C002_01
## 1.021627 1.015482 1.006245GLM C003_01 ~ n_children_in_household + C002_01 + H002
glm_child <- glm(C003_01 ~ n_children_in_household + C002_01 + H002, family = "binomial", data = child_ica_dummy)
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + H002,
## family = "binomial", data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0122 -1.2391 0.7184 0.8909 1.2724
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.424111 0.016894 25.10 <2e-16 ***
## n_children_in_household -0.052085 0.002941 -17.71 <2e-16 ***
## C002_01 -0.575354 0.009055 -63.54 <2e-16 ***
## H002 0.503563 0.006129 82.16 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 288199 on 245743 degrees of freedom
## AIC: 288207
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 818.15, df = 8, p-value < 2.2e-16Hunza. C003_01 ~n_children_in_household + C002_01 + H002
glm_child_hunza <- glm(C003_01 ~n_children_in_household + C002_01 + H002, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
ggPredict(glm_child_hunza, se = TRUE, show.summary = TRUE, point = TRUE, colorAsFactor = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + H002,
## family = "binomial", data = child_ica_dummy %>% filter(DID ==
## 266))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5804 0.3373 0.3731 0.4124 0.5843
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.48657 0.48393 7.205 5.82e-13 ***
## n_children_in_household -0.20822 0.06887 -3.023 0.0025 **
## C002_01 0.13022 0.19320 0.674 0.5003
## H002 -0.11589 0.15847 -0.731 0.4646
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 838.79 on 1609 degrees of freedom
## Residual deviance: 829.57 on 1606 degrees of freedom
## AIC: 837.57
##
## Number of Fisher Scoring iterations: 5## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child_hunza$y, y = fitted(glm_child_hunza))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child_hunza$y, fitted(glm_child_hunza)
## X-squared = 12.015, df = 8, p-value = 0.1505C002_01 chi Districts in Gilgit-Baltistan
child_ica_dummy %>% filter(RID == 6) %>% summarize(unique(DID))sapply(260:266, function(dist){
glm_child_dist <- glm(C003_01 ~ C002_01, family = "binomial", data = child_ica_dummy %>% filter(RID == 6, DID == dist))
glm_child_dist_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy %>% filter(RID == 6, DID == dist))
anova <- anova(glm_child_dist_null, glm_child_dist, test = "Chisq")
data.frame(result = anova$`Pr(>Chi)`[2], DID = dist, chi_0.05 = anova$`Pr(>Chi)`[2] >= 0.05)
})## [,1] [,2] [,3] [,4] [,5] [,6]
## result 0.02323511 1.711351e-125 0.02518717 0.04226537 0.1507538 0.7348869
## DID 260 261 262 263 264 265
## chi_0.05 FALSE FALSE FALSE FALSE TRUE TRUE
## [,7]
## result 0.6463022
## DID 266
## chi_0.05 TRUE
Within Gilgit-Baltistan, 3 of 7 districts including Hunza has Chi > 0.05
GLM C003_01 ~ C002_01 and Anova for All Districts
DID_unique <- child_ica_dummy %>% summarize(unique(DID))
DID_unique <- as.matrix(DID_unique)
DID_unique <- as.numeric(DID_unique[,1])
dist_logist <- sapply(DID_unique, function(dist){
glm_child_dist <- glm(C003_01 ~ C002_01,
family = "binomial",
data = child_ica_dummy %>%
filter(DID == dist))
glm_child_dist_null <- glm(C003_01~1,
family = "binomial",
data = child_ica_dummy %>%
filter(DID == dist))
anova <- anova(glm_child_dist_null, glm_child_dist, test = "Chisq")
data.frame(DID = dist,
chi = anova$`Pr(>Chi)`[2],
chi_largerThan_0.05 = anova$`Pr(>Chi)`[2] >= 0.05,
chi_largerThan_0.1 = anova$`Pr(>Chi)`[2] >= 0.1)
})
anova_dist <- as.data.frame(as.tibble(t(dist_logist)))## Warning: `as.tibble()` is deprecated as of tibble 2.0.0.
## Please use `as_tibble()` instead.
## The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.anova_dist$DID <- as.numeric(anova_dist$DID)
anova_dist$chi <- as.numeric(anova_dist$chi)
anova_dist$chi_largerThan_0.1 <- as.logical(anova_dist$chi_largerThan_0.1)
anova_dist$chi_largerThan_0.05 <- as.logical(anova_dist$chi_largerThan_0.05)anova_dist %>%
summarize(total_chi_largerThan_0.05 = sum(as.numeric(chi_largerThan_0.05)),
ratio_0.05 = sum(as.numeric(chi_largerThan_0.05))/length(chi_largerThan_0.05),
total_chi_largerThan_0.1 = sum(as.numeric(chi_largerThan_0.1)),
ratio_0.1 = sum(as.numeric(chi_largerThan_0.1))/length(chi_largerThan_0.1))DIDs with chi >= 0.05
anova_dist %>%
filter(chi_largerThan_0.05 == TRUE) %>%
select(-chi_largerThan_0.1)DID chi > 0.05
DID_chi_0.05 <- anova_dist %>%
filter(chi_largerThan_0.05 == TRUE) %>%
select(DID) %>%
pull()
DID_chi_0.05## [1] 146 151 156 158 159 162 163 164 167 171 173 176 178 179 189 196 245 257 264
## [20] 265 266 267 268 269 270 271 272 273 274 276DIDs with chi >= 0.1
anova_dist %>%
filter(chi_largerThan_0.1 == TRUE) %>%
select(-chi_largerThan_0.05)DID chi > 0.1
DID_chi_0.1 <- anova_dist %>%
filter(chi_largerThan_0.1 == TRUE) %>%
select(DID) %>%
pull()DID with C002 == -1, chi > 0.05
child %>%
filter(C002 == -1) %>%
group_by(DID) %>%
mutate(avg_learning = mean(C010, na.rm = TRUE)) %>%
ggplot(aes(DID, avg_learning)) +
geom_point(data = child %>%
filter(C002 == -1 & DID == DID_chi_0.05) %>%
group_by(DID) %>%
mutate(avg_learning = mean(C010, na.rm = TRUE)), size = 4, color = "red") +
geom_point(aes(color = RID)) +
geom_text(aes(label = DID), nudge_x = 5, check_overlap = TRUE)## Warning in DID == DID_chi_0.05: 長いオブジェクトの長さが短いオブジェクトの長さの
## 倍数になっていません
C001 & enroll ; DID chi > 0.1
child %>%
filter(DID == DID_chi_0.1) %>%
group_by(DID, C001) %>%
mutate(enroll = mean(C003 == 3)) %>%
ggplot(aes(C001, enroll)) +
geom_point() +
geom_smooth() +
facet_wrap(DID~.)## Warning in DID == DID_chi_0.1: 長いオブジェクトの長さが短いオブジェクトの長さの
## 倍数になっていません## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
GLM C003_01 ~ n_children_in_household + H002 + C002_01 + as.factor(DID)
glm_child <- glm(C003_01 ~ n_children_in_household + H002 + C002_01 + as.factor(DID), family = "binomial", data = child_ica_dummy)
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + H002 + C002_01 +
## as.factor(DID), family = "binomial", data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5433 -1.0857 0.6008 0.8455 1.6963
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.856387 0.090069 20.611 < 2e-16 ***
## n_children_in_household -0.030048 0.003248 -9.252 < 2e-16 ***
## H002 0.238646 0.007614 31.342 < 2e-16 ***
## C002_01 -0.661321 0.009514 -69.509 < 2e-16 ***
## as.factor(DID)147 -0.531421 0.106816 -4.975 6.52e-07 ***
## as.factor(DID)148 -0.080366 0.111073 -0.724 0.469342
## as.factor(DID)149 -0.729406 0.102725 -7.101 1.24e-12 ***
## as.factor(DID)150 -0.755588 0.101817 -7.421 1.16e-13 ***
## as.factor(DID)151 -0.188233 0.111184 -1.693 0.090458 .
## as.factor(DID)152 -0.294774 0.115283 -2.557 0.010559 *
## as.factor(DID)153 -0.876057 0.103921 -8.430 < 2e-16 ***
## as.factor(DID)154 -0.635280 0.105517 -6.021 1.74e-09 ***
## as.factor(DID)155 -0.824648 0.101025 -8.163 3.27e-16 ***
## as.factor(DID)156 -1.295679 0.115699 -11.199 < 2e-16 ***
## as.factor(DID)157 -0.457369 0.110809 -4.128 3.67e-05 ***
## as.factor(DID)158 -0.353198 0.107855 -3.275 0.001058 **
## as.factor(DID)159 -1.312255 0.106407 -12.332 < 2e-16 ***
## as.factor(DID)160 -0.699375 0.104766 -6.676 2.46e-11 ***
## as.factor(DID)161 -1.308519 0.097458 -13.426 < 2e-16 ***
## as.factor(DID)162 0.210991 0.125085 1.687 0.091645 .
## as.factor(DID)163 0.071172 0.127802 0.557 0.577599
## as.factor(DID)164 -0.508366 0.113661 -4.473 7.73e-06 ***
## as.factor(DID)165 -0.991673 0.104159 -9.521 < 2e-16 ***
## as.factor(DID)166 -0.869402 0.104533 -8.317 < 2e-16 ***
## as.factor(DID)167 0.526340 0.130238 4.041 5.31e-05 ***
## as.factor(DID)169 -1.655333 0.099225 -16.683 < 2e-16 ***
## as.factor(DID)170 -0.488745 0.108583 -4.501 6.76e-06 ***
## as.factor(DID)171 -0.436012 0.110567 -3.943 8.03e-05 ***
## as.factor(DID)172 -0.624901 0.106396 -5.873 4.27e-09 ***
## as.factor(DID)173 -0.306239 0.109770 -2.790 0.005274 **
## as.factor(DID)174 -0.895481 0.105491 -8.489 < 2e-16 ***
## as.factor(DID)175 -1.320879 0.101365 -13.031 < 2e-16 ***
## as.factor(DID)176 -0.464176 0.114670 -4.048 5.17e-05 ***
## as.factor(DID)177 -0.853066 0.102982 -8.284 < 2e-16 ***
## as.factor(DID)178 0.182187 0.117181 1.555 0.120006
## as.factor(DID)179 -0.718075 0.105393 -6.813 9.54e-12 ***
## as.factor(DID)180 -0.673160 0.105975 -6.352 2.12e-10 ***
## as.factor(DID)181 -0.158811 0.115371 -1.377 0.168658
## as.factor(DID)182 -1.252132 0.100397 -12.472 < 2e-16 ***
## as.factor(DID)183 -0.624218 0.107630 -5.800 6.64e-09 ***
## as.factor(DID)184 -1.279621 0.099900 -12.809 < 2e-16 ***
## as.factor(DID)185 0.315727 0.116482 2.711 0.006718 **
## as.factor(DID)186 -1.527947 0.099616 -15.338 < 2e-16 ***
## as.factor(DID)187 -1.365833 0.101106 -13.509 < 2e-16 ***
## as.factor(DID)188 -1.201238 0.100289 -11.978 < 2e-16 ***
## as.factor(DID)189 -0.279213 0.108191 -2.581 0.009858 **
## as.factor(DID)190 -0.984810 0.108473 -9.079 < 2e-16 ***
## as.factor(DID)191 -0.856359 0.108294 -7.908 2.62e-15 ***
## as.factor(DID)192 -0.907494 0.102627 -8.843 < 2e-16 ***
## as.factor(DID)193 -0.062637 0.110415 -0.567 0.570519
## as.factor(DID)194 -1.641845 0.099810 -16.450 < 2e-16 ***
## as.factor(DID)195 -1.800501 0.100992 -17.828 < 2e-16 ***
## as.factor(DID)196 -0.643909 0.109777 -5.866 4.47e-09 ***
## as.factor(DID)197 -1.333062 0.100752 -13.231 < 2e-16 ***
## as.factor(DID)198 -1.750612 0.099615 -17.574 < 2e-16 ***
## as.factor(DID)199 0.411202 0.116763 3.522 0.000429 ***
## as.factor(DID)200 -1.504418 0.102715 -14.647 < 2e-16 ***
## as.factor(DID)202 -1.430222 0.100851 -14.182 < 2e-16 ***
## as.factor(DID)203 -1.244656 0.099211 -12.546 < 2e-16 ***
## as.factor(DID)204 -0.864444 0.103348 -8.364 < 2e-16 ***
## as.factor(DID)205 -1.475565 0.098277 -15.014 < 2e-16 ***
## as.factor(DID)206 -1.502885 0.098520 -15.255 < 2e-16 ***
## as.factor(DID)207 -1.931182 0.100015 -19.309 < 2e-16 ***
## as.factor(DID)208 -1.433055 0.099404 -14.417 < 2e-16 ***
## as.factor(DID)209 -0.835136 0.108254 -7.715 1.21e-14 ***
## as.factor(DID)210 -1.656130 0.097887 -16.919 < 2e-16 ***
## as.factor(DID)211 -0.940851 0.107889 -8.721 < 2e-16 ***
## as.factor(DID)212 -1.184811 0.102333 -11.578 < 2e-16 ***
## as.factor(DID)213 -1.492092 0.098622 -15.129 < 2e-16 ***
## as.factor(DID)214 -1.980250 0.099212 -19.960 < 2e-16 ***
## as.factor(DID)215 -1.277002 0.096980 -13.168 < 2e-16 ***
## as.factor(DID)216 -1.475047 0.101753 -14.496 < 2e-16 ***
## as.factor(DID)217 -1.645835 0.099970 -16.463 < 2e-16 ***
## as.factor(DID)218 -1.391871 0.097592 -14.262 < 2e-16 ***
## as.factor(DID)219 -1.414559 0.100711 -14.046 < 2e-16 ***
## as.factor(DID)220 -2.067994 0.096336 -21.466 < 2e-16 ***
## as.factor(DID)221 -2.314209 0.101510 -22.798 < 2e-16 ***
## as.factor(DID)222 -1.569588 0.097845 -16.042 < 2e-16 ***
## as.factor(DID)223 -1.497175 0.105467 -14.196 < 2e-16 ***
## as.factor(DID)224 -2.024431 0.099120 -20.424 < 2e-16 ***
## as.factor(DID)225 -1.626735 0.099947 -16.276 < 2e-16 ***
## as.factor(DID)226 -1.380602 0.102822 -13.427 < 2e-16 ***
## as.factor(DID)227 -1.587866 0.098118 -16.183 < 2e-16 ***
## as.factor(DID)228 -2.149597 0.103253 -20.819 < 2e-16 ***
## as.factor(DID)229 -2.016092 0.101742 -19.816 < 2e-16 ***
## as.factor(DID)230 -1.952694 0.099633 -19.599 < 2e-16 ***
## as.factor(DID)231 -1.323551 0.098863 -13.388 < 2e-16 ***
## as.factor(DID)232 -0.149362 0.107653 -1.387 0.165307
## as.factor(DID)233 -2.172729 0.104344 -20.823 < 2e-16 ***
## as.factor(DID)234 -1.481327 0.105267 -14.072 < 2e-16 ***
## as.factor(DID)235 -0.728578 0.102880 -7.082 1.42e-12 ***
## as.factor(DID)236 -1.011111 0.103059 -9.811 < 2e-16 ***
## as.factor(DID)237 -0.768246 0.105928 -7.253 4.09e-13 ***
## as.factor(DID)238 0.092540 0.116242 0.796 0.425973
## as.factor(DID)239 -0.895697 0.102414 -8.746 < 2e-16 ***
## as.factor(DID)240 -1.139789 0.102174 -11.155 < 2e-16 ***
## as.factor(DID)241 -2.331117 0.098219 -23.734 < 2e-16 ***
## as.factor(DID)242 -1.090313 0.103367 -10.548 < 2e-16 ***
## as.factor(DID)243 -1.092509 0.108519 -10.067 < 2e-16 ***
## as.factor(DID)244 -0.389501 0.110870 -3.513 0.000443 ***
## as.factor(DID)245 0.307911 0.125277 2.458 0.013978 *
## as.factor(DID)246 -1.693232 0.100045 -16.925 < 2e-16 ***
## as.factor(DID)247 -1.233913 0.107645 -11.463 < 2e-16 ***
## as.factor(DID)248 -0.789366 0.100028 -7.891 2.99e-15 ***
## as.factor(DID)249 -0.023336 0.124757 -0.187 0.851620
## as.factor(DID)250 -0.687107 0.105155 -6.534 6.39e-11 ***
## as.factor(DID)251 -0.540857 0.106874 -5.061 4.18e-07 ***
## as.factor(DID)252 -0.860171 0.103757 -8.290 < 2e-16 ***
## as.factor(DID)253 -0.422480 0.111991 -3.772 0.000162 ***
## as.factor(DID)254 -1.716154 0.115165 -14.902 < 2e-16 ***
## as.factor(DID)255 -0.718627 0.108954 -6.596 4.23e-11 ***
## as.factor(DID)256 -0.192197 0.106990 -1.796 0.072430 .
## as.factor(DID)257 0.525470 0.140459 3.741 0.000183 ***
## as.factor(DID)258 -1.393632 0.099084 -14.065 < 2e-16 ***
## as.factor(DID)259 -0.803014 0.107179 -7.492 6.77e-14 ***
## as.factor(DID)260 -0.030875 0.108454 -0.285 0.775887
## as.factor(DID)261 -2.203777 0.098347 -22.408 < 2e-16 ***
## as.factor(DID)262 -0.562816 0.103736 -5.425 5.78e-08 ***
## as.factor(DID)263 -0.168283 0.107682 -1.563 0.118105
## as.factor(DID)264 -0.378301 0.105640 -3.581 0.000342 ***
## as.factor(DID)265 0.045178 0.114401 0.395 0.692909
## as.factor(DID)266 0.651750 0.130083 5.010 5.44e-07 ***
## as.factor(DID)267 -0.138255 0.106774 -1.295 0.195374
## as.factor(DID)268 -0.998930 0.104179 -9.589 < 2e-16 ***
## as.factor(DID)269 0.089197 0.112064 0.796 0.426058
## as.factor(DID)270 0.047179 0.111679 0.422 0.672695
## as.factor(DID)271 0.316533 0.115617 2.738 0.006186 **
## as.factor(DID)272 0.145384 0.112480 1.293 0.196175
## as.factor(DID)273 -0.121213 0.109283 -1.109 0.267356
## as.factor(DID)274 0.006667 0.120782 0.055 0.955983
## as.factor(DID)275 -1.031188 0.101880 -10.122 < 2e-16 ***
## as.factor(DID)276 -0.061414 0.106435 -0.577 0.563931
## as.factor(DID)277 -0.253596 0.146904 -1.726 0.084300 .
## as.factor(DID)278 -0.665286 0.098548 -6.751 1.47e-11 ***
## as.factor(DID)279 -1.754758 0.100067 -17.536 < 2e-16 ***
## as.factor(DID)280 -0.906296 0.102311 -8.858 < 2e-16 ***
## as.factor(DID)281 -0.667260 0.101980 -6.543 6.03e-11 ***
## as.factor(DID)282 -0.588057 0.109290 -5.381 7.42e-08 ***
## as.factor(DID)284 -0.676702 0.104745 -6.460 1.04e-10 ***
## as.factor(DID)287 -0.907293 0.104730 -8.663 < 2e-16 ***
## as.factor(DID)289 -0.917523 0.101097 -9.076 < 2e-16 ***
## as.factor(DID)290 -0.888790 0.104415 -8.512 < 2e-16 ***
## as.factor(DID)315 -0.736484 0.115424 -6.381 1.76e-10 ***
## as.factor(DID)316 -1.195621 0.103364 -11.567 < 2e-16 ***
## as.factor(DID)318 -1.593066 0.105466 -15.105 < 2e-16 ***
## as.factor(DID)319 -2.132163 0.099025 -21.532 < 2e-16 ***
## as.factor(DID)320 -1.398009 0.104773 -13.343 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 269755 on 245600 degrees of freedom
## AIC: 270049
##
## Number of Fisher Scoring iterations: 5## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 330.28, df = 8, p-value < 2.2e-16GLM C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(DID)
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(DID), family = "binomial", data = child_ica_dummy)
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## C002_01 + as.factor(DID), family = "binomial", data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5059 -1.0668 0.6014 0.8352 1.7174
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.161512 0.088576 24.403 < 2e-16 ***
## n_children_in_household -0.028508 0.003251 -8.768 < 2e-16 ***
## PR004_PR009_01 0.347071 0.010500 33.054 < 2e-16 ***
## C002_01 -0.664193 0.009519 -69.776 < 2e-16 ***
## as.factor(DID)147 -0.607598 0.106738 -5.692 1.25e-08 ***
## as.factor(DID)148 -0.093994 0.111048 -0.846 0.397314
## as.factor(DID)149 -0.804572 0.102678 -7.836 4.65e-15 ***
## as.factor(DID)150 -0.784029 0.101828 -7.700 1.37e-14 ***
## as.factor(DID)151 -0.190780 0.111171 -1.716 0.086146 .
## as.factor(DID)152 -0.334471 0.115245 -2.902 0.003705 **
## as.factor(DID)153 -0.900790 0.103870 -8.672 < 2e-16 ***
## as.factor(DID)154 -0.678256 0.105494 -6.429 1.28e-10 ***
## as.factor(DID)155 -0.842393 0.101011 -8.340 < 2e-16 ***
## as.factor(DID)156 -1.313467 0.115592 -11.363 < 2e-16 ***
## as.factor(DID)157 -0.452119 0.110799 -4.081 4.49e-05 ***
## as.factor(DID)158 -0.343909 0.107871 -3.188 0.001432 **
## as.factor(DID)159 -1.309531 0.106342 -12.314 < 2e-16 ***
## as.factor(DID)160 -0.738367 0.104702 -7.052 1.76e-12 ***
## as.factor(DID)161 -1.277624 0.097450 -13.111 < 2e-16 ***
## as.factor(DID)162 0.140673 0.125046 1.125 0.260600
## as.factor(DID)163 0.076054 0.127784 0.595 0.551728
## as.factor(DID)164 -0.566415 0.113648 -4.984 6.23e-07 ***
## as.factor(DID)165 -0.978953 0.104104 -9.404 < 2e-16 ***
## as.factor(DID)166 -0.830884 0.104540 -7.948 1.90e-15 ***
## as.factor(DID)167 0.509703 0.130242 3.914 9.10e-05 ***
## as.factor(DID)169 -1.723715 0.099078 -17.398 < 2e-16 ***
## as.factor(DID)170 -0.472852 0.108538 -4.357 1.32e-05 ***
## as.factor(DID)171 -0.437550 0.110541 -3.958 7.55e-05 ***
## as.factor(DID)172 -0.638525 0.106358 -6.004 1.93e-09 ***
## as.factor(DID)173 -0.267159 0.109787 -2.433 0.014957 *
## as.factor(DID)174 -0.956716 0.105408 -9.076 < 2e-16 ***
## as.factor(DID)175 -1.331763 0.101311 -13.145 < 2e-16 ***
## as.factor(DID)176 -0.472402 0.114611 -4.122 3.76e-05 ***
## as.factor(DID)177 -0.852129 0.102954 -8.277 < 2e-16 ***
## as.factor(DID)178 0.202014 0.117141 1.725 0.084612 .
## as.factor(DID)179 -0.718205 0.105390 -6.815 9.44e-12 ***
## as.factor(DID)180 -0.678592 0.105962 -6.404 1.51e-10 ***
## as.factor(DID)181 -0.117571 0.115344 -1.019 0.308054
## as.factor(DID)182 -1.388829 0.100242 -13.855 < 2e-16 ***
## as.factor(DID)183 -0.659636 0.107584 -6.131 8.71e-10 ***
## as.factor(DID)184 -1.307346 0.099855 -13.092 < 2e-16 ***
## as.factor(DID)185 0.289557 0.116541 2.485 0.012970 *
## as.factor(DID)186 -1.693746 0.099333 -17.051 < 2e-16 ***
## as.factor(DID)187 -1.457246 0.100967 -14.433 < 2e-16 ***
## as.factor(DID)188 -1.301699 0.100182 -12.993 < 2e-16 ***
## as.factor(DID)189 -0.357912 0.108160 -3.309 0.000936 ***
## as.factor(DID)190 -0.994167 0.108433 -9.168 < 2e-16 ***
## as.factor(DID)191 -0.905377 0.108243 -8.364 < 2e-16 ***
## as.factor(DID)192 -1.059675 0.102504 -10.338 < 2e-16 ***
## as.factor(DID)193 -0.060710 0.110471 -0.550 0.582622
## as.factor(DID)194 -1.683985 0.099732 -16.885 < 2e-16 ***
## as.factor(DID)195 -1.882723 0.100866 -18.666 < 2e-16 ***
## as.factor(DID)196 -0.698907 0.109777 -6.367 1.93e-10 ***
## as.factor(DID)197 -1.429311 0.100649 -14.201 < 2e-16 ***
## as.factor(DID)198 -1.785280 0.099553 -17.933 < 2e-16 ***
## as.factor(DID)199 0.351822 0.116662 3.016 0.002564 **
## as.factor(DID)200 -1.457248 0.102727 -14.186 < 2e-16 ***
## as.factor(DID)202 -1.476437 0.100767 -14.652 < 2e-16 ***
## as.factor(DID)203 -1.442589 0.099023 -14.568 < 2e-16 ***
## as.factor(DID)204 -0.894943 0.103239 -8.669 < 2e-16 ***
## as.factor(DID)205 -1.587176 0.098221 -16.159 < 2e-16 ***
## as.factor(DID)206 -1.652157 0.098176 -16.828 < 2e-16 ***
## as.factor(DID)207 -2.029097 0.099744 -20.343 < 2e-16 ***
## as.factor(DID)208 -1.513908 0.099299 -15.246 < 2e-16 ***
## as.factor(DID)209 -1.027678 0.107955 -9.519 < 2e-16 ***
## as.factor(DID)210 -1.723325 0.097692 -17.640 < 2e-16 ***
## as.factor(DID)211 -1.029961 0.107737 -9.560 < 2e-16 ***
## as.factor(DID)212 -1.344713 0.102072 -13.174 < 2e-16 ***
## as.factor(DID)213 -1.582224 0.098485 -16.066 < 2e-16 ***
## as.factor(DID)214 -2.095527 0.099005 -21.166 < 2e-16 ***
## as.factor(DID)215 -1.359969 0.096766 -14.054 < 2e-16 ***
## as.factor(DID)216 -1.675878 0.101392 -16.529 < 2e-16 ***
## as.factor(DID)217 -1.709895 0.099764 -17.139 < 2e-16 ***
## as.factor(DID)218 -1.634273 0.097160 -16.820 < 2e-16 ***
## as.factor(DID)219 -1.542831 0.100494 -15.352 < 2e-16 ***
## as.factor(DID)220 -2.103022 0.096242 -21.851 < 2e-16 ***
## as.factor(DID)221 -2.455716 0.101216 -24.262 < 2e-16 ***
## as.factor(DID)222 -1.692441 0.097663 -17.329 < 2e-16 ***
## as.factor(DID)223 -1.604057 0.105314 -15.231 < 2e-16 ***
## as.factor(DID)224 -2.148013 0.098930 -21.712 < 2e-16 ***
## as.factor(DID)225 -1.852791 0.099548 -18.612 < 2e-16 ***
## as.factor(DID)226 -1.388128 0.102732 -13.512 < 2e-16 ***
## as.factor(DID)227 -1.677606 0.097855 -17.144 < 2e-16 ***
## as.factor(DID)228 -2.257037 0.102988 -21.916 < 2e-16 ***
## as.factor(DID)229 -1.971458 0.101725 -19.380 < 2e-16 ***
## as.factor(DID)230 -2.137518 0.099303 -21.525 < 2e-16 ***
## as.factor(DID)231 -1.403955 0.098699 -14.225 < 2e-16 ***
## as.factor(DID)232 -0.293014 0.107407 -2.728 0.006370 **
## as.factor(DID)233 -2.270591 0.104146 -21.802 < 2e-16 ***
## as.factor(DID)234 -1.583978 0.105021 -15.082 < 2e-16 ***
## as.factor(DID)235 -0.832266 0.102738 -8.101 5.46e-16 ***
## as.factor(DID)236 -1.105162 0.102937 -10.736 < 2e-16 ***
## as.factor(DID)237 -0.764733 0.105896 -7.222 5.14e-13 ***
## as.factor(DID)238 0.248673 0.116251 2.139 0.032427 *
## as.factor(DID)239 -0.895892 0.102392 -8.750 < 2e-16 ***
## as.factor(DID)240 -1.242176 0.102128 -12.163 < 2e-16 ***
## as.factor(DID)241 -2.369040 0.098180 -24.129 < 2e-16 ***
## as.factor(DID)242 -1.101073 0.103317 -10.657 < 2e-16 ***
## as.factor(DID)243 -1.057710 0.108492 -9.749 < 2e-16 ***
## as.factor(DID)244 -0.560656 0.110773 -5.061 4.16e-07 ***
## as.factor(DID)245 0.261619 0.125199 2.090 0.036652 *
## as.factor(DID)246 -1.706840 0.100013 -17.066 < 2e-16 ***
## as.factor(DID)247 -1.234545 0.107606 -11.473 < 2e-16 ***
## as.factor(DID)248 -0.945059 0.099728 -9.476 < 2e-16 ***
## as.factor(DID)249 0.026476 0.124731 0.212 0.831901
## as.factor(DID)250 -0.721227 0.105067 -6.864 6.68e-12 ***
## as.factor(DID)251 -0.688719 0.106677 -6.456 1.07e-10 ***
## as.factor(DID)252 -0.913442 0.103706 -8.808 < 2e-16 ***
## as.factor(DID)253 -0.511879 0.111901 -4.574 4.78e-06 ***
## as.factor(DID)254 -1.762981 0.115162 -15.309 < 2e-16 ***
## as.factor(DID)255 -0.856150 0.108863 -7.864 3.71e-15 ***
## as.factor(DID)256 -0.251852 0.106913 -2.356 0.018489 *
## as.factor(DID)257 0.505590 0.140392 3.601 0.000317 ***
## as.factor(DID)258 -1.562184 0.098968 -15.785 < 2e-16 ***
## as.factor(DID)259 -0.846290 0.107113 -7.901 2.77e-15 ***
## as.factor(DID)260 -0.132057 0.108378 -1.218 0.223037
## as.factor(DID)261 -2.301424 0.098132 -23.452 < 2e-16 ***
## as.factor(DID)262 -0.725316 0.103528 -7.006 2.45e-12 ***
## as.factor(DID)263 -0.414257 0.107373 -3.858 0.000114 ***
## as.factor(DID)264 -0.444065 0.105615 -4.205 2.62e-05 ***
## as.factor(DID)265 -0.075139 0.114318 -0.657 0.510998
## as.factor(DID)266 0.615342 0.130073 4.731 2.24e-06 ***
## as.factor(DID)267 -0.257368 0.106773 -2.410 0.015934 *
## as.factor(DID)268 -1.115655 0.104103 -10.717 < 2e-16 ***
## as.factor(DID)269 -0.143548 0.111944 -1.282 0.199729
## as.factor(DID)270 -0.052250 0.111599 -0.468 0.639648
## as.factor(DID)271 0.056815 0.115452 0.492 0.622643
## as.factor(DID)272 -0.088445 0.112363 -0.787 0.431202
## as.factor(DID)273 -0.338363 0.109176 -3.099 0.001940 **
## as.factor(DID)274 -0.169419 0.120699 -1.404 0.160423
## as.factor(DID)275 -1.110405 0.101896 -10.897 < 2e-16 ***
## as.factor(DID)276 -0.187979 0.106426 -1.766 0.077348 .
## as.factor(DID)277 -0.200188 0.146869 -1.363 0.172873
## as.factor(DID)278 -0.791947 0.098415 -8.047 8.49e-16 ***
## as.factor(DID)279 -1.831521 0.099957 -18.323 < 2e-16 ***
## as.factor(DID)280 -1.045823 0.102210 -10.232 < 2e-16 ***
## as.factor(DID)281 -0.817280 0.101859 -8.024 1.03e-15 ***
## as.factor(DID)282 -0.777911 0.109042 -7.134 9.75e-13 ***
## as.factor(DID)284 -0.782806 0.104522 -7.489 6.92e-14 ***
## as.factor(DID)287 -1.035915 0.104583 -9.905 < 2e-16 ***
## as.factor(DID)289 -0.883532 0.101090 -8.740 < 2e-16 ***
## as.factor(DID)290 -1.049015 0.104241 -10.063 < 2e-16 ***
## as.factor(DID)315 -0.730629 0.115409 -6.331 2.44e-10 ***
## as.factor(DID)316 -1.090704 0.103373 -10.551 < 2e-16 ***
## as.factor(DID)318 -1.666857 0.105254 -15.837 < 2e-16 ***
## as.factor(DID)319 -2.273787 0.098779 -23.019 < 2e-16 ***
## as.factor(DID)320 -1.525834 0.104652 -14.580 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 269656 on 245600 degrees of freedom
## AIC: 269950
##
## Number of Fisher Scoring iterations: 5## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 415.55, df = 8, p-value < 2.2e-16exponential transformation
exp(glm_child$coefficients)## (Intercept) n_children_in_household PR004_PR009_01
## 8.68426215 0.97189458 1.41491756
## C002_01 as.factor(DID)147 as.factor(DID)148
## 0.51468855 0.54465757 0.91028798
## as.factor(DID)149 as.factor(DID)150 as.factor(DID)151
## 0.44727950 0.45656288 0.82631471
## as.factor(DID)152 as.factor(DID)153 as.factor(DID)154
## 0.71571648 0.40624870 0.50750152
## as.factor(DID)155 as.factor(DID)156 as.factor(DID)157
## 0.43067852 0.26888624 0.63627844
## as.factor(DID)158 as.factor(DID)159 as.factor(DID)160
## 0.70899315 0.26994651 0.47789348
## as.factor(DID)161 as.factor(DID)162 as.factor(DID)163
## 0.27869861 1.15104847 1.07902058
## as.factor(DID)164 as.factor(DID)165 as.factor(DID)166
## 0.56755636 0.37570439 0.43566418
## as.factor(DID)167 as.factor(DID)169 as.factor(DID)170
## 1.66479663 0.17840212 0.62322241
## as.factor(DID)171 as.factor(DID)172 as.factor(DID)173
## 0.64561636 0.52807089 0.76555125
## as.factor(DID)174 as.factor(DID)175 as.factor(DID)176
## 0.38415219 0.26401140 0.62350291
## as.factor(DID)177 as.factor(DID)178 as.factor(DID)179
## 0.42650577 1.22386457 0.48762652
## as.factor(DID)180 as.factor(DID)181 as.factor(DID)182
## 0.50733088 0.88907702 0.24936704
## as.factor(DID)183 as.factor(DID)184 as.factor(DID)185
## 0.51703930 0.27053703 1.33583600
## as.factor(DID)186 as.factor(DID)187 as.factor(DID)188
## 0.18382953 0.23287662 0.27206914
## as.factor(DID)189 as.factor(DID)190 as.factor(DID)191
## 0.69913479 0.37003145 0.40438937
## as.factor(DID)192 as.factor(DID)193 as.factor(DID)194
## 0.34656849 0.94109585 0.18563282
## as.factor(DID)195 as.factor(DID)196 as.factor(DID)197
## 0.15217520 0.49712841 0.23947388
## as.factor(DID)198 as.factor(DID)199 as.factor(DID)200
## 0.16775016 1.42165604 0.23287626
## as.factor(DID)202 as.factor(DID)203 as.factor(DID)204
## 0.22845019 0.23631521 0.40863100
## as.factor(DID)205 as.factor(DID)206 as.factor(DID)207
## 0.20450233 0.19163614 0.13145413
## as.factor(DID)208 as.factor(DID)209 as.factor(DID)210
## 0.22004826 0.35783680 0.17847180
## as.factor(DID)211 as.factor(DID)212 as.factor(DID)213
## 0.35702078 0.26061459 0.20551759
## as.factor(DID)214 as.factor(DID)215 as.factor(DID)216
## 0.12300538 0.25666877 0.18714385
## as.factor(DID)217 as.factor(DID)218 as.factor(DID)219
## 0.18088477 0.19509408 0.21377515
## as.factor(DID)220 as.factor(DID)221 as.factor(DID)222
## 0.12208693 0.08580172 0.18406963
## as.factor(DID)223 as.factor(DID)224 as.factor(DID)225
## 0.20107914 0.11671582 0.15679900
## as.factor(DID)226 as.factor(DID)227 as.factor(DID)228
## 0.24954195 0.18682063 0.10466013
## as.factor(DID)229 as.factor(DID)230 as.factor(DID)231
## 0.13925361 0.11794719 0.24562354
## as.factor(DID)232 as.factor(DID)233 as.factor(DID)234
## 0.74601182 0.10325112 0.20515743
## as.factor(DID)235 as.factor(DID)236 as.factor(DID)237
## 0.43506233 0.33115733 0.46545817
## as.factor(DID)238 as.factor(DID)239 as.factor(DID)240
## 1.28232283 0.40824317 0.28875517
## as.factor(DID)241 as.factor(DID)242 as.factor(DID)243
## 0.09357048 0.33251418 0.34725018
## as.factor(DID)244 as.factor(DID)245 as.factor(DID)246
## 0.57083429 1.29903108 0.18143825
## as.factor(DID)247 as.factor(DID)248 as.factor(DID)249
## 0.29096723 0.38865667 1.02682949
## as.factor(DID)250 as.factor(DID)251 as.factor(DID)252
## 0.48615558 0.50221916 0.40114117
## as.factor(DID)253 as.factor(DID)254 as.factor(DID)255
## 0.59936809 0.17153272 0.42479420
## as.factor(DID)256 as.factor(DID)257 as.factor(DID)258
## 0.77736012 1.65796325 0.20967757
## as.factor(DID)259 as.factor(DID)260 as.factor(DID)261
## 0.42900378 0.87629061 0.10011614
## as.factor(DID)262 as.factor(DID)263 as.factor(DID)264
## 0.48417163 0.66083096 0.64142358
## as.factor(DID)265 as.factor(DID)266 as.factor(DID)267
## 0.92761446 1.85028857 0.77308363
## as.factor(DID)268 as.factor(DID)269 as.factor(DID)270
## 0.32770047 0.86627896 0.94909197
## as.factor(DID)271 as.factor(DID)272 as.factor(DID)273
## 1.05845992 0.91535372 0.71293627
## as.factor(DID)274 as.factor(DID)275 as.factor(DID)276
## 0.84415517 0.32942564 0.82863226
## as.factor(DID)277 as.factor(DID)278 as.factor(DID)279
## 0.81857708 0.45296190 0.16016976
## as.factor(DID)280 as.factor(DID)281 as.factor(DID)282
## 0.35140253 0.44163131 0.45936450
## as.factor(DID)284 as.factor(DID)287 as.factor(DID)289
## 0.45712131 0.35490148 0.41332046
## as.factor(DID)290 as.factor(DID)315 as.factor(DID)316
## 0.35028265 0.48160583 0.33598000
## as.factor(DID)318 as.factor(DID)319 as.factor(DID)320
## 0.18883967 0.10292172 0.21743960confidence interval (intercept and coefficient)
# confint(glm_child, level = 0.95)exponential transformation of confidence interval (odds ratio of intercept and coefficient)
# exp(confint(glm_child, level = 0.95))AIC
extractAIC(glm_child)## [1] 147.0 269950.4BIC
extractAIC(glm_child, k = log(nrow(glm_child$data)))## [1] 147 271481effectiveness of explanatory variables
glm_child_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy)
anova(glm_child_null, glm_child, test = "Chisq")variables selection
# step(glm_child_null, direction = "both",
# scope = (~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(DID)))multicolinearity
vif(glm_child)## GVIF Df GVIF^(1/(2*Df))
## n_children_in_household 1.146849 1 1.070910
## PR004_PR009_01 1.246852 1 1.116625
## C002_01 1.024292 1 1.012073
## as.factor(DID) 1.441880 143 1.001280Districts with P-Values Bigger Than 0.05 and Coefficient
glm_summary <- glm_child %>% summary()
glm_coef <- as.data.frame(glm_summary$coefficients)
glm_coef %>% filter(`Pr(>|z|)` > 0.05)Districts with P-Values Bigger Than 0.05
glm_coef %>% filter(`Pr(>|z|)` > 0.05) %>% t() %>% .[-c(1:5),]## as.factor(DID)148 as.factor(DID)151 as.factor(DID)162 as.factor(DID)163
## as.factor(DID)178 as.factor(DID)181 as.factor(DID)193 as.factor(DID)249
## as.factor(DID)260 as.factor(DID)265 as.factor(DID)269 as.factor(DID)270
## as.factor(DID)271 as.factor(DID)272 as.factor(DID)274 as.factor(DID)276
## as.factor(DID)277dists_not_fit <- c(148, 151, 162, 163, 178, 181, 193, 249, 260, 265, 269, 270, 271, 272, 274, 276, 277)
child_ica_dummy %>% filter(DID == dists_not_fit) %>% select(DID, DNAME) %>% summarize(DID = unique(DID), DNAME = unique(DNAME))## Warning in DID == dists_not_fit: 長いオブジェクトの長さが短いオブジェクトの長さ
## の倍数になっていませんDistricts with P-Values Bigger Than 0.001 and Coefficient
glm_summary <- glm_child %>% summary()
glm_coef <- as.data.frame(glm_summary$coefficients)
glm_coef %>% filter(`Pr(>|z|)` > 0.001)Districts with P-Values Bigger Than 0.001
glm_coef %>% filter(`Pr(>|z|)` > 0.001) %>% t() %>% .[-c(1:5),]## as.factor(DID)148 as.factor(DID)151 as.factor(DID)152 as.factor(DID)158
## as.factor(DID)162 as.factor(DID)163 as.factor(DID)173 as.factor(DID)178
## as.factor(DID)181 as.factor(DID)185 as.factor(DID)193 as.factor(DID)199
## as.factor(DID)232 as.factor(DID)238 as.factor(DID)245 as.factor(DID)249
## as.factor(DID)256 as.factor(DID)260 as.factor(DID)265 as.factor(DID)267
## as.factor(DID)269 as.factor(DID)270 as.factor(DID)271 as.factor(DID)272
## as.factor(DID)273 as.factor(DID)274 as.factor(DID)276 as.factor(DID)277dists_not_fit <- c(148, 151, 152, 158, 162, 163, 173, 178, 181, 185, 193, 199, 232, 238, 245, 249, 256, 260, 265, 267, 269, 270, 271, 272, 273, 274, 276, 277)
child_ica_dummy %>% filter(DID == dists_not_fit) %>% select(DID, DNAME) %>% summarize(DID = unique(DID), DNAME = unique(DNAME))## Warning in DID == dists_not_fit: 長いオブジェクトの長さが短いオブジェクトの長さ
## の倍数になっていませんDists_not_fit. GLM C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01
All of dists_not_fit
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(VID), family = "binomial", data = child_ica_dummy %>% filter(DID %in% dists_not_fit))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## C002_01 + as.factor(VID), family = "binomial", data = child_ica_dummy %>%
## filter(DID %in% dists_not_fit))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9975 0.2123 0.4372 0.5768 1.4738
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.702857 0.485396 3.508 0.000451 ***
## n_children_in_household 0.036001 0.012295 2.928 0.003410 **
## PR004_PR009_01 0.085176 0.040443 2.106 0.035197 *
## C002_01 -0.181653 0.028957 -6.273 3.54e-10 ***
## as.factor(VID)4937 -0.475516 0.669974 -0.710 0.477857
## as.factor(VID)4938 1.793771 1.123676 1.596 0.110412
## as.factor(VID)4939 1.106393 0.872792 1.268 0.204924
## as.factor(VID)4940 0.394658 0.715379 0.552 0.581170
## as.factor(VID)4941 -0.345331 0.662635 -0.521 0.602264
## as.factor(VID)4942 1.897602 1.122701 1.690 0.090987 .
## as.factor(VID)4943 -1.210639 0.566444 -2.137 0.032577 *
## as.factor(VID)4944 0.575980 0.774371 0.744 0.456995
## as.factor(VID)4945 0.078486 0.614769 0.128 0.898411
## as.factor(VID)4946 0.527867 0.712682 0.741 0.458890
## as.factor(VID)4947 -0.398360 0.623935 -0.638 0.523172
## as.factor(VID)4948 1.326896 0.869342 1.526 0.126929
## as.factor(VID)4949 -0.512888 0.613484 -0.836 0.403141
## as.factor(VID)4950 2.151462 1.119483 1.922 0.054627 .
## as.factor(VID)4951 0.858204 0.768481 1.117 0.264100
## as.factor(VID)4952 -0.381300 0.580474 -0.657 0.511260
## as.factor(VID)4953 1.603477 0.866352 1.851 0.064193 .
## as.factor(VID)4954 -1.093095 0.577997 -1.891 0.058601 .
## as.factor(VID)4955 -0.050238 0.633927 -0.079 0.936835
## as.factor(VID)4956 -1.544766 0.538628 -2.868 0.004131 **
## as.factor(VID)4957 0.048790 0.682060 0.072 0.942973
## as.factor(VID)4958 -0.811464 0.565449 -1.435 0.151264
## as.factor(VID)4959 15.776122 688.099301 0.023 0.981708
## as.factor(VID)4966 -0.047350 0.616762 -0.077 0.938805
## as.factor(VID)4991 15.732527 482.918751 0.033 0.974011
## as.factor(VID)4992 0.927659 0.767274 1.209 0.226650
## as.factor(VID)4993 0.508508 0.775083 0.656 0.511780
## as.factor(VID)4994 2.147531 1.119164 1.919 0.055001 .
## as.factor(VID)4995 0.475163 0.713868 0.666 0.505656
## as.factor(VID)4996 0.240871 0.678371 0.355 0.722535
## as.factor(VID)4997 1.789771 1.124045 1.592 0.111326
## as.factor(VID)4998 0.468174 0.775809 0.603 0.546200
## as.factor(VID)4999 1.570511 0.866595 1.812 0.069943 .
## as.factor(VID)5000 0.492290 0.712538 0.691 0.489631
## as.factor(VID)5001 0.661238 0.771970 0.857 0.391689
## as.factor(VID)5002 -0.341784 0.641394 -0.533 0.594119
## as.factor(VID)14940 0.561585 0.712159 0.789 0.430365
## as.factor(VID)14941 0.316169 0.647988 0.488 0.625604
## as.factor(VID)14942 -0.630604 0.604462 -1.043 0.296833
## as.factor(VID)14943 0.467471 0.674064 0.694 0.487989
## as.factor(VID)14944 0.461904 0.674483 0.685 0.493453
## as.factor(VID)14945 2.049224 1.120379 1.829 0.067393 .
## as.factor(VID)14947 2.057430 1.120162 1.837 0.066250 .
## as.factor(VID)14950 0.296842 0.648604 0.458 0.647194
## as.factor(VID)14953 -0.062520 0.634450 -0.099 0.921501
## as.factor(VID)14954 -0.374583 0.588340 -0.637 0.524335
## as.factor(VID)14955 0.327471 0.676200 0.484 0.628186
## as.factor(VID)14960 1.302113 0.869561 1.497 0.134279
## as.factor(VID)14961 1.189938 0.871560 1.365 0.172160
## as.factor(VID)14963 0.255933 0.677879 0.378 0.705765
## as.factor(VID)14968 0.908239 0.767804 1.183 0.236847
## as.factor(VID)14974 15.765453 602.859981 0.026 0.979137
## as.factor(VID)14989 -1.924316 0.557627 -3.451 0.000559 ***
## as.factor(VID)14992 0.626077 0.712945 0.878 0.379859
## as.factor(VID)15044 0.031497 0.615695 0.051 0.959201
## as.factor(VID)15045 0.507392 0.713008 0.712 0.476699
## as.factor(VID)15046 -0.193405 0.605654 -0.319 0.749475
## as.factor(VID)15047 1.124288 0.764798 1.470 0.141549
## as.factor(VID)15048 15.738471 475.904668 0.033 0.973618
## as.factor(VID)15049 0.239993 0.649539 0.369 0.711768
## as.factor(VID)15050 1.059699 0.765894 1.384 0.166478
## as.factor(VID)15051 0.087376 0.614193 0.142 0.886873
## as.factor(VID)15052 2.162975 1.119121 1.933 0.053268 .
## as.factor(VID)15053 1.549129 0.866415 1.788 0.073780 .
## as.factor(VID)15054 2.442462 1.117262 2.186 0.028807 *
## as.factor(VID)15055 15.760623 465.867725 0.034 0.973012
## as.factor(VID)15056 0.016514 0.653380 0.025 0.979836
## as.factor(VID)15057 -0.311420 0.608224 -0.512 0.608641
## as.factor(VID)15058 0.560069 0.672762 0.832 0.405131
## as.factor(VID)15059 -0.745893 0.581300 -1.283 0.199441
## as.factor(VID)15060 -0.317363 0.598154 -0.531 0.595716
## as.factor(VID)15061 0.087698 0.601177 0.146 0.884018
## as.factor(VID)15062 0.168002 0.629668 0.267 0.789615
## as.factor(VID)15063 -0.595903 0.577550 -1.032 0.302176
## as.factor(VID)15064 0.343953 0.626848 0.549 0.583210
## as.factor(VID)15065 1.323116 0.762586 1.735 0.082734 .
## as.factor(VID)15066 -0.058239 0.603119 -0.097 0.923073
## as.factor(VID)15067 -0.085077 0.604256 -0.141 0.888032
## as.factor(VID)15068 0.447159 0.674373 0.663 0.507284
## as.factor(VID)15069 -0.621296 0.593878 -1.046 0.295484
## as.factor(VID)15071 -0.217174 0.595648 -0.365 0.715409
## as.factor(VID)15072 -0.707899 0.588761 -1.202 0.229227
## as.factor(VID)15073 0.336505 0.648069 0.519 0.603592
## as.factor(VID)15074 -0.156413 0.593777 -0.263 0.792226
## as.factor(VID)15075 2.215300 1.118873 1.980 0.047710 *
## as.factor(VID)15076 -0.500689 0.582727 -0.859 0.390220
## as.factor(VID)15077 -0.725065 0.574332 -1.262 0.206787
## as.factor(VID)15078 0.031167 0.614725 0.051 0.959565
## as.factor(VID)15079 -0.994810 0.564925 -1.761 0.078245 .
## as.factor(VID)15080 0.780988 0.708754 1.102 0.270498
## as.factor(VID)15081 -0.492756 0.569364 -0.865 0.386792
## as.factor(VID)15082 0.035333 0.615607 0.057 0.954230
## as.factor(VID)15083 0.904192 0.707535 1.278 0.201268
## as.factor(VID)15084 -0.178550 0.584903 -0.305 0.760165
## as.factor(VID)15085 -0.076212 0.603747 -0.126 0.899548
## as.factor(VID)15086 -0.148628 0.605298 -0.246 0.806034
## as.factor(VID)15087 2.279669 1.118420 2.038 0.041521 *
## as.factor(VID)15088 0.525381 0.645341 0.814 0.415580
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## as.factor(VID)47296 2.487725 1.116943 2.227 0.025930 *
## as.factor(VID)47297 -0.583901 0.577203 -1.012 0.311728
## as.factor(VID)47298 0.386300 0.676141 0.571 0.567776
## as.factor(VID)47299 -0.085749 0.576048 -0.149 0.881666
## as.factor(VID)47300 -0.964665 0.587783 -1.641 0.100757
## as.factor(VID)47301 0.088483 0.601117 0.147 0.882975
## as.factor(VID)47302 2.257053 1.118955 2.017 0.043684 *
## as.factor(VID)47303 -1.764014 0.523868 -3.367 0.000759 ***
## as.factor(VID)47304 15.789555 518.895611 0.030 0.975725
## as.factor(VID)47305 0.283663 0.648692 0.437 0.661905
## as.factor(VID)47306 -0.163797 0.595391 -0.275 0.783233
## as.factor(VID)47308 0.778488 0.669757 1.162 0.245096
## as.factor(VID)47312 -0.805214 0.560241 -1.437 0.150643
## as.factor(VID)47313 0.955393 0.706826 1.352 0.176482
## as.factor(VID)47318 -0.345227 0.562451 -0.614 0.539354
## as.factor(VID)47327 2.562371 1.116831 2.294 0.021772 *
## as.factor(VID)47332 0.392205 0.611203 0.642 0.521073
## as.factor(VID)47338 0.070826 0.601863 0.118 0.906323
## as.factor(VID)47339 -1.521528 0.538599 -2.825 0.004728 **
## as.factor(VID)47345 15.805475 519.074198 0.030 0.975709
## as.factor(VID)47352 0.149910 0.581358 0.258 0.796514
## as.factor(VID)47355 15.722019 486.682243 0.032 0.974229
## as.factor(VID)47356 0.492063 0.646052 0.762 0.446271
## as.factor(VID)47409 -0.531779 0.570153 -0.933 0.350977
## as.factor(VID)47582 -0.658947 0.551248 -1.195 0.231941
## as.factor(VID)57619 -0.072776 0.604272 -0.120 0.904138
## as.factor(VID)57636 15.796454 523.532430 0.030 0.975929
## as.factor(VID)57641 2.176013 1.119156 1.944 0.051855 .
## as.factor(VID)57643 2.295024 1.118420 2.052 0.040167 *
## as.factor(VID)57644 0.568646 0.672774 0.845 0.397985
## as.factor(VID)57646 -0.295638 0.597261 -0.495 0.620607
## as.factor(VID)57648 0.837509 0.669554 1.251 0.210991
## as.factor(VID)67891 0.031481 0.632009 0.050 0.960273
## as.factor(VID)67936 15.775771 641.018933 0.025 0.980366
## as.factor(VID)67944 -0.125314 0.618708 -0.203 0.839494
## as.factor(VID)67952 1.560857 1.126862 1.385 0.166011
## as.factor(VID)67958 15.781843 582.770380 0.027 0.978395
## as.factor(VID)67961 15.821342 678.043819 0.023 0.981384
## as.factor(VID)67963 -0.318569 0.597643 -0.533 0.594004
## as.factor(VID)67965 0.977977 0.766847 1.275 0.202195
## as.factor(VID)68080 1.127498 0.764962 1.474 0.140501
## as.factor(VID)68083 1.081570 0.765144 1.414 0.157493
## as.factor(VID)78079 1.374272 0.762279 1.803 0.071412 .
## as.factor(VID)78205 15.784982 589.157368 0.027 0.978625
## as.factor(VID)78214 0.026923 0.632497 0.043 0.966048
## as.factor(VID)78215 1.550169 0.866733 1.789 0.073692 .
## as.factor(VID)78231 2.288708 1.118076 2.047 0.040657 *
## as.factor(VID)78232 -0.372018 0.573657 -0.649 0.516659
## as.factor(VID)78233 15.739795 498.063149 0.032 0.974789
## as.factor(VID)78234 0.301659 0.629624 0.479 0.631861
## as.factor(VID)78235 15.744419 506.152991 0.031 0.975185
## as.factor(VID)78236 -0.709223 0.563773 -1.258 0.208394
## as.factor(VID)78237 0.004801 0.591431 0.008 0.993523
## as.factor(VID)78238 15.752656 514.709173 0.031 0.975585
## as.factor(VID)78239 -1.988252 0.550100 -3.614 0.000301 ***
## as.factor(VID)78240 -1.310650 0.556127 -2.357 0.018436 *
## as.factor(VID)78241 15.743716 510.359310 0.031 0.975391
## as.factor(VID)78242 0.872311 0.709179 1.230 0.218686
## as.factor(VID)78243 -1.283590 0.530650 -2.419 0.015567 *
## as.factor(VID)78244 -1.246037 0.536366 -2.323 0.020173 *
## as.factor(VID)78245 15.743881 523.589969 0.030 0.976012
## as.factor(VID)78246 -1.106187 0.549741 -2.012 0.044199 *
## as.factor(VID)78247 15.708400 416.447914 0.038 0.969911
## as.factor(VID)78248 -0.033518 0.602772 -0.056 0.955655
## as.factor(VID)78249 -0.995070 0.547815 -1.816 0.069304 .
## as.factor(VID)78250 15.785718 486.524569 0.032 0.974116
## as.factor(VID)78251 -1.576847 0.561215 -2.810 0.004959 **
## as.factor(VID)78252 -0.695917 0.581068 -1.198 0.231052
## as.factor(VID)78253 2.262027 1.118899 2.022 0.043212 *
## as.factor(VID)78254 1.751807 0.865985 2.023 0.043083 *
## as.factor(VID)78255 15.830001 589.309921 0.027 0.978570
## as.factor(VID)78256 15.863330 564.771063 0.028 0.977592
## as.factor(VID)78257 15.791484 459.523260 0.034 0.972586
## as.factor(VID)78258 -2.267406 0.547946 -4.138 3.50e-05 ***
## as.factor(VID)78259 -1.129091 0.563852 -2.002 0.045235 *
## as.factor(VID)78260 -0.243663 0.596842 -0.408 0.683088
## as.factor(VID)78261 -0.293771 0.623220 -0.471 0.637373
## as.factor(VID)78262 15.818045 688.251716 0.023 0.981664
## as.factor(VID)78263 2.338700 1.117720 2.092 0.036404 *
## as.factor(VID)78266 0.087066 0.602409 0.145 0.885083
## as.factor(VID)78267 0.842959 0.769826 1.095 0.273517
## as.factor(VID)78268 -1.412754 0.545757 -2.589 0.009636 **
## as.factor(VID)78269 -1.541264 0.542124 -2.843 0.004469 **
## as.factor(VID)78270 2.481996 1.117535 2.221 0.026354 *
## as.factor(VID)78271 15.821523 486.556290 0.033 0.974059
## as.factor(VID)78272 2.299845 1.119029 2.055 0.039858 *
## as.factor(VID)78273 1.385150 0.762101 1.818 0.069134 .
## as.factor(VID)78276 1.780017 0.865352 2.057 0.039688 *
## as.factor(VID)78277 -0.338993 0.598968 -0.566 0.571420
## as.factor(VID)78279 0.273566 0.599968 0.456 0.648413
## as.factor(VID)78283 0.214436 0.589283 0.364 0.715938
## as.factor(VID)78285 15.803299 490.303674 0.032 0.974287
## as.factor(VID)78286 15.791458 447.561366 0.035 0.971854
## as.factor(VID)78288 2.346093 1.118685 2.097 0.035977 *
## as.factor(VID)78291 0.140548 0.601302 0.234 0.815188
## as.factor(VID)78292 15.795047 625.050119 0.025 0.979840
## as.factor(VID)78295 2.389088 1.118482 2.136 0.032679 *
## as.factor(VID)78298 15.761151 510.187737 0.031 0.975355
## as.factor(VID)78300 1.620474 0.866619 1.870 0.061500 .
## as.factor(VID)78301 1.554667 0.866750 1.794 0.072865 .
## as.factor(VID)78302 -0.927305 0.558781 -1.660 0.097012 .
## as.factor(VID)78303 2.248605 1.119261 2.009 0.044536 *
## as.factor(VID)78309 0.186767 0.722130 0.259 0.795918
## as.factor(VID)78312 0.642393 0.671884 0.956 0.339018
## as.factor(VID)78313 15.753804 514.346726 0.031 0.975566
## as.factor(VID)78542 -0.711497 0.575134 -1.237 0.216051
## as.factor(VID)78592 0.892926 0.707791 1.262 0.207105
## as.factor(VID)78593 0.258996 0.599295 0.432 0.665619
## as.factor(VID)78594 1.655915 0.866624 1.911 0.056035 .
## as.factor(VID)78595 0.600128 0.645188 0.930 0.352289
## as.factor(VID)78596 -0.057576 0.584194 -0.099 0.921491
## as.factor(VID)78597 0.272549 0.628454 0.434 0.664520
## as.factor(VID)78598 1.237141 0.764049 1.619 0.105406
## as.factor(VID)78599 0.992409 0.707280 1.403 0.160576
## as.factor(VID)78600 -0.119587 0.659266 -0.181 0.856058
## as.factor(VID)78601 0.618837 0.624635 0.991 0.321823
## as.factor(VID)78602 -0.117786 0.619920 -0.190 0.849307
## as.factor(VID)78604 -0.026189 0.633961 -0.041 0.967049
## as.factor(VID)78605 -1.527879 0.537106 -2.845 0.004446 **
## as.factor(VID)78606 0.260018 0.599983 0.433 0.664743
## as.factor(VID)78607 1.617264 0.866599 1.866 0.062011 .
## as.factor(VID)78608 -1.831664 0.578401 -3.167 0.001541 **
## as.factor(VID)78609 0.037324 0.582709 0.064 0.948929
## as.factor(VID)78611 -0.132736 0.570417 -0.233 0.815994
## as.factor(VID)78613 0.032167 0.603057 0.053 0.957461
## as.factor(VID)78614 1.646500 1.126585 1.461 0.143879
## as.factor(VID)78616 -1.151708 0.588214 -1.958 0.050233 .
## as.factor(VID)78617 0.808014 0.710055 1.138 0.255137
## as.factor(VID)78619 0.939569 0.767507 1.224 0.220883
## as.factor(VID)78638 0.743653 0.710549 1.047 0.295289
## as.factor(VID)78643 0.084692 0.581771 0.146 0.884256
## as.factor(VID)78649 -1.113646 0.546270 -2.039 0.041486 *
## as.factor(VID)78650 1.422021 0.868324 1.638 0.101492
## as.factor(VID)78651 1.302766 0.703869 1.851 0.064189 .
## as.factor(VID)78652 -0.598458 0.566787 -1.056 0.291024
## as.factor(VID)78654 -0.366908 0.609070 -0.602 0.546904
## as.factor(VID)78683 2.355678 1.117978 2.107 0.035110 *
## as.factor(VID)78687 0.833156 0.708954 1.175 0.239919
## as.factor(VID)78689 0.168448 0.613980 0.274 0.783812
## as.factor(VID)78691 0.708219 0.671415 1.055 0.291509
## as.factor(VID)78693 0.580813 0.646215 0.899 0.368764
## as.factor(VID)78694 -0.107306 0.592706 -0.181 0.856332
## as.factor(VID)78695 0.085388 0.614725 0.139 0.889526
## as.factor(VID)78696 0.225318 0.629479 0.358 0.720385
## as.factor(VID)78698 1.345215 0.762844 1.763 0.077829 .
## as.factor(VID)78699 0.021065 0.615911 0.034 0.972717
## as.factor(VID)78700 0.382819 0.676908 0.566 0.571706
## as.factor(VID)78701 -0.482984 0.583569 -0.828 0.407875
## as.factor(VID)78703 0.555820 0.645901 0.861 0.389494
## as.factor(VID)78704 1.114903 0.705257 1.581 0.113913
## as.factor(VID)78705 0.442459 0.625850 0.707 0.479584
## as.factor(VID)78706 0.363225 0.648378 0.560 0.575339
## as.factor(VID)78708 0.059886 0.632239 0.095 0.924536
## as.factor(VID)78709 0.166935 0.651516 0.256 0.797777
## as.factor(VID)78711 0.703494 0.670946 1.049 0.294403
## as.factor(VID)78712 0.108207 0.630624 0.172 0.863762
## as.factor(VID)78713 1.321680 0.869359 1.520 0.128437
## as.factor(VID)78717 1.671492 0.865589 1.931 0.053477 .
## as.factor(VID)78718 0.189608 0.629416 0.301 0.763227
## as.factor(VID)78739 0.275108 0.628039 0.438 0.661355
## as.factor(VID)78741 0.452720 0.647100 0.700 0.484169
## as.factor(VID)78742 -0.344545 0.588934 -0.585 0.558526
## as.factor(VID)78744 -0.066329 0.604724 -0.110 0.912660
## as.factor(VID)78745 0.187966 0.614178 0.306 0.759570
## as.factor(VID)78746 0.215480 0.629842 0.342 0.732263
## as.factor(VID)78748 0.116072 0.614800 0.189 0.850253
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 37106 on 47598 degrees of freedom
## Residual deviance: 32845 on 46789 degrees of freedom
## AIC: 34465
##
## Number of Fisher Scoring iterations: 16## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 6.8991, df = 8, p-value = 0.5476148
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 148))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## C002_01, family = "binomial", data = child_ica_dummy %>%
## filter(DID == 148))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1602 0.4780 0.5202 0.5651 0.6721
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.08964 0.22845 9.147 <2e-16 ***
## n_children_in_household -0.05985 0.04872 -1.228 0.219
## PR004_PR009_01 0.20142 0.15237 1.322 0.186
## C002_01 -0.29794 0.13730 -2.170 0.030 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1462.2 on 1851 degrees of freedom
## Residual deviance: 1454.1 on 1848 degrees of freedom
## AIC: 1462.1
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 0.70355, df = 8, p-value = 0.9995151
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 151))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## C002_01, family = "binomial", data = child_ica_dummy %>%
## filter(DID == 151))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0776 0.5003 0.5051 0.5338 0.7164
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.42858 0.25219 5.665 1.47e-08 ***
## n_children_in_household -0.01015 0.05416 -0.187 0.851379
## PR004_PR009_01 0.61702 0.16072 3.839 0.000123 ***
## C002_01 -0.12832 0.13800 -0.930 0.352425
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1445.1 on 1793 degrees of freedom
## Residual deviance: 1430.1 on 1790 degrees of freedom
## AIC: 1438.1
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 26.905, df = 8, p-value = 0.0007342260
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 260))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## C002_01, family = "binomial", data = child_ica_dummy %>%
## filter(DID == 260))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1956 0.4753 0.5348 0.5847 0.7639
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.48695 0.21355 11.646 <2e-16 ***
## n_children_in_household -0.08535 0.03699 -2.308 0.0210 *
## PR004_PR009_01 -0.27317 0.14617 -1.869 0.0616 .
## C002_01 -0.27779 0.12815 -2.168 0.0302 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1650.2 on 2004 degrees of freedom
## Residual deviance: 1636.2 on 2001 degrees of freedom
## AIC: 1644.2
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 15.649, df = 8, p-value = 0.0477260, VID
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + factor(VID), family = "binomial", data = child_ica_dummy %>% filter(DID == 260))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## C002_01 + factor(VID), family = "binomial", data = child_ica_dummy %>%
## filter(DID == 260))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.98580 0.00014 0.42879 0.57612 1.18235
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.67422 0.48297 3.466 0.000527 ***
## n_children_in_household 0.03091 0.04526 0.683 0.494630
## PR004_PR009_01 0.06956 0.18637 0.373 0.708963
## C002_01 -0.12462 0.14000 -0.890 0.373392
## factor(VID)47295 -1.07596 0.53590 -2.008 0.044668 *
## factor(VID)47296 2.51948 1.10231 2.286 0.022276 *
## factor(VID)47297 -0.54884 0.54600 -1.005 0.314801
## factor(VID)47298 0.40838 0.64832 0.630 0.528759
## factor(VID)47299 -0.03990 0.55358 -0.072 0.942536
## factor(VID)47300 -0.94556 0.55578 -1.701 0.088881 .
## factor(VID)47301 0.12755 0.57501 0.222 0.824453
## factor(VID)47302 2.28506 1.10787 2.063 0.039154 *
## factor(VID)47303 -1.72351 0.50253 -3.430 0.000604 ***
## factor(VID)47304 16.81541 855.93786 0.020 0.984326
## factor(VID)47305 0.31982 0.62245 0.514 0.607388
## factor(VID)47306 -0.13887 0.57337 -0.242 0.808632
## factor(VID)47308 0.81913 0.64618 1.268 0.204919
## factor(VID)47312 -0.76830 0.53045 -1.448 0.147507
## factor(VID)47313 0.98803 0.68135 1.450 0.147027
## factor(VID)47318 -0.31342 0.53646 -0.584 0.559061
## factor(VID)47327 2.58615 1.10413 2.342 0.019168 *
## factor(VID)47332 0.42155 0.59249 0.711 0.476779
## factor(VID)47338 0.09982 0.57314 0.174 0.861735
## factor(VID)47339 -1.50129 0.51737 -2.902 0.003711 **
## factor(VID)47345 16.82728 856.13930 0.020 0.984319
## factor(VID)47352 0.19564 0.56564 0.346 0.729437
## factor(VID)47355 16.76490 802.63292 0.021 0.983335
## factor(VID)47356 0.52232 0.62125 0.841 0.400484
## factor(VID)47409 -0.49782 0.53881 -0.924 0.355524
## factor(VID)47582 -0.63324 0.52455 -1.207 0.227346
## factor(VID)57636 16.81954 863.54271 0.019 0.984460
## factor(VID)57643 2.31954 1.10185 2.105 0.035279 *
## factor(VID)57644 0.59762 0.64630 0.925 0.355130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1650.2 on 2004 degrees of freedom
## Residual deviance: 1365.7 on 1972 degrees of freedom
## AIC: 1431.7
##
## Number of Fisher Scoring iterations: 17## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 2.569, df = 8, p-value = 0.9584Without dists_not_fit. GLM C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(DID)
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(DID), family = "binomial", data = child_ica_dummy %>% filter(!DID %in% dists_not_fit))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## C002_01 + as.factor(DID), family = "binomial", data = child_ica_dummy %>%
## filter(!DID %in% dists_not_fit))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5291 -1.1412 0.6592 0.8972 1.7474
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.20023 0.08886 24.761 < 2e-16 ***
## n_children_in_household -0.03221 0.00342 -9.417 < 2e-16 ***
## PR004_PR009_01 0.36964 0.01108 33.351 < 2e-16 ***
## C002_01 -0.73250 0.01017 -72.036 < 2e-16 ***
## as.factor(DID)147 -0.61110 0.10692 -5.715 1.09e-08 ***
## as.factor(DID)149 -0.80774 0.10286 -7.853 4.07e-15 ***
## as.factor(DID)150 -0.78424 0.10201 -7.688 1.50e-14 ***
## as.factor(DID)153 -0.90444 0.10406 -8.692 < 2e-16 ***
## as.factor(DID)154 -0.68180 0.10568 -6.452 1.11e-10 ***
## as.factor(DID)155 -0.84481 0.10119 -8.349 < 2e-16 ***
## as.factor(DID)156 -1.33487 0.11582 -11.525 < 2e-16 ***
## as.factor(DID)157 -0.45598 0.11098 -4.108 3.98e-05 ***
## as.factor(DID)159 -1.32982 0.10655 -12.481 < 2e-16 ***
## as.factor(DID)160 -0.73750 0.10489 -7.031 2.05e-12 ***
## as.factor(DID)161 -1.27916 0.09762 -13.103 < 2e-16 ***
## as.factor(DID)164 -0.57774 0.11384 -5.075 3.87e-07 ***
## as.factor(DID)165 -0.98013 0.10429 -9.398 < 2e-16 ***
## as.factor(DID)166 -0.83499 0.10473 -7.973 1.55e-15 ***
## as.factor(DID)167 0.50880 0.13040 3.902 9.55e-05 ***
## as.factor(DID)169 -1.72476 0.09928 -17.373 < 2e-16 ***
## as.factor(DID)170 -0.47695 0.10872 -4.387 1.15e-05 ***
## as.factor(DID)171 -0.44109 0.11072 -3.984 6.78e-05 ***
## as.factor(DID)172 -0.63932 0.10654 -6.001 1.96e-09 ***
## as.factor(DID)174 -0.96366 0.10561 -9.125 < 2e-16 ***
## as.factor(DID)175 -1.33897 0.10151 -13.191 < 2e-16 ***
## as.factor(DID)176 -0.48230 0.11479 -4.201 2.65e-05 ***
## as.factor(DID)177 -0.85401 0.10314 -8.280 < 2e-16 ***
## as.factor(DID)179 -0.72202 0.10558 -6.839 7.98e-12 ***
## as.factor(DID)180 -0.68083 0.10615 -6.414 1.42e-10 ***
## as.factor(DID)182 -1.39805 0.10043 -13.920 < 2e-16 ***
## as.factor(DID)183 -0.65956 0.10778 -6.120 9.39e-10 ***
## as.factor(DID)184 -1.30777 0.10005 -13.071 < 2e-16 ***
## as.factor(DID)186 -1.69771 0.09953 -17.058 < 2e-16 ***
## as.factor(DID)187 -1.46210 0.10117 -14.452 < 2e-16 ***
## as.factor(DID)188 -1.30548 0.10037 -13.006 < 2e-16 ***
## as.factor(DID)189 -0.35890 0.10834 -3.313 0.000924 ***
## as.factor(DID)190 -0.99623 0.10865 -9.169 < 2e-16 ***
## as.factor(DID)191 -0.90900 0.10845 -8.382 < 2e-16 ***
## as.factor(DID)192 -1.06589 0.10269 -10.380 < 2e-16 ***
## as.factor(DID)194 -1.68630 0.09992 -16.876 < 2e-16 ***
## as.factor(DID)195 -1.88739 0.10107 -18.674 < 2e-16 ***
## as.factor(DID)196 -0.70672 0.10997 -6.426 1.31e-10 ***
## as.factor(DID)197 -1.43308 0.10084 -14.211 < 2e-16 ***
## as.factor(DID)198 -1.79500 0.09975 -17.996 < 2e-16 ***
## as.factor(DID)200 -1.46069 0.10294 -14.190 < 2e-16 ***
## as.factor(DID)202 -1.48817 0.10096 -14.740 < 2e-16 ***
## as.factor(DID)203 -1.44779 0.09921 -14.593 < 2e-16 ***
## as.factor(DID)204 -0.89171 0.10343 -8.621 < 2e-16 ***
## as.factor(DID)205 -1.59378 0.09841 -16.196 < 2e-16 ***
## as.factor(DID)206 -1.65528 0.09837 -16.827 < 2e-16 ***
## as.factor(DID)207 -2.02987 0.09995 -20.309 < 2e-16 ***
## as.factor(DID)208 -1.51565 0.09950 -15.233 < 2e-16 ***
## as.factor(DID)209 -1.03418 0.10817 -9.560 < 2e-16 ***
## as.factor(DID)210 -1.72023 0.09790 -17.572 < 2e-16 ***
## as.factor(DID)211 -1.03761 0.10795 -9.611 < 2e-16 ***
## as.factor(DID)212 -1.34488 0.10228 -13.149 < 2e-16 ***
## as.factor(DID)213 -1.58217 0.09868 -16.033 < 2e-16 ***
## as.factor(DID)214 -2.10360 0.09920 -21.205 < 2e-16 ***
## as.factor(DID)215 -1.35788 0.09697 -14.003 < 2e-16 ***
## as.factor(DID)216 -1.68497 0.10160 -16.585 < 2e-16 ***
## as.factor(DID)217 -1.71022 0.09997 -17.107 < 2e-16 ***
## as.factor(DID)218 -1.64404 0.09735 -16.889 < 2e-16 ***
## as.factor(DID)219 -1.54732 0.10069 -15.366 < 2e-16 ***
## as.factor(DID)220 -2.09984 0.09644 -21.774 < 2e-16 ***
## as.factor(DID)221 -2.45952 0.10142 -24.251 < 2e-16 ***
## as.factor(DID)222 -1.69695 0.09785 -17.341 < 2e-16 ***
## as.factor(DID)223 -1.61343 0.10554 -15.288 < 2e-16 ***
## as.factor(DID)224 -2.15683 0.09912 -21.759 < 2e-16 ***
## as.factor(DID)225 -1.86448 0.09974 -18.693 < 2e-16 ***
## as.factor(DID)226 -1.38124 0.10295 -13.417 < 2e-16 ***
## as.factor(DID)227 -1.67171 0.09806 -17.047 < 2e-16 ***
## as.factor(DID)228 -2.25942 0.10320 -21.893 < 2e-16 ***
## as.factor(DID)229 -1.96803 0.10194 -19.306 < 2e-16 ***
## as.factor(DID)230 -2.14701 0.09950 -21.579 < 2e-16 ***
## as.factor(DID)231 -1.40315 0.09889 -14.188 < 2e-16 ***
## as.factor(DID)233 -2.27841 0.10437 -21.830 < 2e-16 ***
## as.factor(DID)234 -1.58568 0.10526 -15.065 < 2e-16 ***
## as.factor(DID)235 -0.83483 0.10293 -8.111 5.02e-16 ***
## as.factor(DID)236 -1.10526 0.10314 -10.717 < 2e-16 ***
## as.factor(DID)237 -0.76287 0.10610 -7.190 6.46e-13 ***
## as.factor(DID)239 -0.89327 0.10257 -8.708 < 2e-16 ***
## as.factor(DID)240 -1.24717 0.10232 -12.189 < 2e-16 ***
## as.factor(DID)241 -2.37464 0.09837 -24.140 < 2e-16 ***
## as.factor(DID)242 -1.09923 0.10351 -10.619 < 2e-16 ***
## as.factor(DID)243 -1.07306 0.10870 -9.872 < 2e-16 ***
## as.factor(DID)244 -0.56597 0.11096 -5.101 3.38e-07 ***
## as.factor(DID)246 -1.71773 0.10021 -17.142 < 2e-16 ***
## as.factor(DID)247 -1.23790 0.10783 -11.480 < 2e-16 ***
## as.factor(DID)248 -0.94198 0.09993 -9.427 < 2e-16 ***
## as.factor(DID)250 -0.72232 0.10526 -6.862 6.77e-12 ***
## as.factor(DID)251 -0.68896 0.10687 -6.447 1.14e-10 ***
## as.factor(DID)252 -0.91843 0.10390 -8.840 < 2e-16 ***
## as.factor(DID)253 -0.51896 0.11209 -4.630 3.66e-06 ***
## as.factor(DID)254 -1.76683 0.11543 -15.306 < 2e-16 ***
## as.factor(DID)255 -0.86631 0.10906 -7.943 1.97e-15 ***
## as.factor(DID)257 0.49870 0.14055 3.548 0.000388 ***
## as.factor(DID)258 -1.57776 0.09915 -15.912 < 2e-16 ***
## as.factor(DID)259 -0.84587 0.10732 -7.882 3.23e-15 ***
## as.factor(DID)261 -2.30250 0.09833 -23.416 < 2e-16 ***
## as.factor(DID)262 -0.72491 0.10371 -6.990 2.75e-12 ***
## as.factor(DID)263 -0.41112 0.10755 -3.822 0.000132 ***
## as.factor(DID)264 -0.44120 0.10580 -4.170 3.04e-05 ***
## as.factor(DID)266 0.61879 0.13022 4.752 2.02e-06 ***
## as.factor(DID)268 -1.12253 0.10429 -10.763 < 2e-16 ***
## as.factor(DID)275 -1.11609 0.10208 -10.933 < 2e-16 ***
## as.factor(DID)278 -0.79219 0.09862 -8.033 9.50e-16 ***
## as.factor(DID)279 -1.83698 0.10016 -18.340 < 2e-16 ***
## as.factor(DID)280 -1.05514 0.10240 -10.304 < 2e-16 ***
## as.factor(DID)281 -0.82032 0.10204 -8.039 9.05e-16 ***
## as.factor(DID)282 -0.78629 0.10925 -7.197 6.16e-13 ***
## as.factor(DID)284 -0.78193 0.10472 -7.467 8.19e-14 ***
## as.factor(DID)287 -1.04173 0.10478 -9.942 < 2e-16 ***
## as.factor(DID)289 -0.88492 0.10127 -8.738 < 2e-16 ***
## as.factor(DID)290 -1.05728 0.10444 -10.123 < 2e-16 ***
## as.factor(DID)315 -0.74213 0.11562 -6.419 1.37e-10 ***
## as.factor(DID)316 -1.09485 0.10357 -10.571 < 2e-16 ***
## as.factor(DID)318 -1.66751 0.10549 -15.807 < 2e-16 ***
## as.factor(DID)319 -2.28183 0.09897 -23.056 < 2e-16 ***
## as.factor(DID)320 -1.53198 0.10486 -14.609 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 253748 on 198147 degrees of freedom
## Residual deviance: 232377 on 198029 degrees of freedom
## AIC: 232615
##
## Number of Fisher Scoring iterations: 5## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 212.13, df = 8, p-value < 2.2e-16fit the momdel for every districts using apply function
All Districts. C003_01 ~ n_children_in_household PR004_PR009_01 + C002_01
fit_all <- sapply(DID_unique, function(id){
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01,
family = "binomial", data = child_ica_dummy %>% filter(DID == id))
# ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
hoslem <- hoslem.test(x = glm_child$y, y = fitted(glm_child))
data.frame(id, hoslem["p.value"])
}) %>% t()
fit_all <- fit_all %>% as.data.frame()
fit_all$id <- fit_all$id %>% as.numeric()
fit_all$p.value <- fit_all$p.value %>% as.numeric()
fit_allDistricts whose model is not fitted well
fit_all %>%
filter(p.value < 0.05)memo
fit the model
glm_child <- glm(C003_01~ C001 + C002_01 + PR004_01, family = "binomial", data = child_ica_dummy)
summary(glm_child)##
## Call:
## glm(formula = C003_01 ~ C001 + C002_01 + PR004_01, family = "binomial",
## data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4716 -1.0691 0.6141 0.8525 1.4442
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.567479 0.012767 -44.45 <2e-16 ***
## C001 0.174066 0.001340 129.90 <2e-16 ***
## C002_01 -0.562952 0.009369 -60.09 <2e-16 ***
## PR004_01 0.788430 0.010620 74.24 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 271929 on 245743 degrees of freedom
## AIC: 271937
##
## Number of Fisher Scoring iterations: 4exponential transformation
exp(glm_child$coefficients)## (Intercept) C001 C002_01 PR004_01
## 0.5669529 1.1901343 0.5695255 2.1999390confidence interval (intercept and coefficient)
confint(glm_child, level = 0.95)## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) -0.5925094 -0.5424624
## C001 0.1714426 0.1766953
## C002_01 -0.5813180 -0.5445920
## PR004_01 0.7676346 0.8092649exponential transformation of confidence interval (odds ratio of intercept and coefficient)
exp(confint(glm_child, level = 0.95))## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 0.5529380 0.5813151
## C001 1.1870160 1.1932675
## C002_01 0.5591609 0.5800784
## PR004_01 2.1546637 2.2462561AIC
extractAIC(glm_child)## [1] 4.0 271937.4BIC
extractAIC(glm_child, k = log(nrow(glm_child$data)))## [1] 4.0 271979.1effectiveness of explanatory variables
glm_child_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy)
anova(glm_child_null, glm_child, test = "Chisq")variables selection
step(glm_child_null, direction = "both",
scope = (~ C001 + C002_01 + PR004_01 + PR009_01))## Start: AIC=299727.8
## C003_01 ~ 1
##
## Df Deviance AIC
## + C001 1 281139 281143
## + PR009_01 1 293719 293723
## + PR004_01 1 295038 295042
## + C002_01 1 295943 295947
## <none> 299726 299728
##
## Step: AIC=281142.5
## C003_01 ~ C001
##
## Df Deviance AIC
## + PR009_01 1 274422 274428
## + PR004_01 1 275560 275566
## + C002_01 1 277803 277809
## <none> 281139 281143
## - C001 1 299726 299728
##
## Step: AIC=274428
## C003_01 ~ C001 + PR009_01
##
## Df Deviance AIC
## + C002_01 1 270765 270773
## + PR004_01 1 272824 272832
## <none> 274422 274428
## - PR009_01 1 281139 281143
## - C001 1 293719 293723
##
## Step: AIC=270773.3
## C003_01 ~ C001 + PR009_01 + C002_01
##
## Df Deviance AIC
## + PR004_01 1 269072 269082
## <none> 270765 270773
## - C002_01 1 274422 274428
## - PR009_01 1 277803 277809
## - C001 1 289624 289630
##
## Step: AIC=269081.6
## C003_01 ~ C001 + PR009_01 + C002_01 + PR004_01
##
## Df Deviance AIC
## <none> 269072 269082
## - PR004_01 1 270765 270773
## - PR009_01 1 271929 271937
## - C002_01 1 272824 272832
## - C001 1 288269 288277##
## Call: glm(formula = C003_01 ~ C001 + PR009_01 + C002_01 + PR004_01,
## family = "binomial", data = child_ica_dummy)
##
## Coefficients:
## (Intercept) C001 PR009_01 C002_01 PR004_01
## -0.7815 0.1760 0.5677 -0.5762 0.4923
##
## Degrees of Freedom: 245746 Total (i.e. Null); 245742 Residual
## Null Deviance: 299700
## Residual Deviance: 269100 AIC: 269100multicolinearity
vif(glm_child)## C001 C002_01 PR004_01
## 1.010465 1.004067 1.013630